{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:52:28Z","timestamp":1775609548770,"version":"3.50.1"},"reference-count":103,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T00:00:00Z","timestamp":1698105600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T00:00:00Z","timestamp":1698105600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Innovations Syst Softw Eng"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s11334-023-00533-2","type":"journal-article","created":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T08:17:50Z","timestamp":1698135470000},"page":"313-332","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A comprehensive survey on machine learning techniques to mobilize multi-camera network for smart surveillance"],"prefix":"10.1007","volume":"21","author":[{"given":"Anandu M.","family":"Dharan","sequence":"first","affiliation":[]},{"given":"Debarka","family":"Mukhopadhyay","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,24]]},"reference":[{"key":"533_CR1","doi-asserted-by":"publisher","unstructured":"Sun B,\u00a0Yuan N,\u00a0Li S,\u00a0Wu S,\u00a0Wang N Human behaviour recognition with mid-level representations for crowd understanding and analysis, IET Image Process. https:\/\/doi.org\/10.1049\/ipr2.12147","DOI":"10.1049\/ipr2.12147"},{"key":"533_CR2","doi-asserted-by":"publisher","unstructured":"Lu J, Yan WQ,\u00a0Nguyen M (2018) Human behaviour recognition using deep learning. In: 2018 15th IEEE international conference on advanced video and signal based surveillance (AVSS), pp 1\u20136. https:\/\/doi.org\/10.1109\/AVSS.2018.8639413","DOI":"10.1109\/AVSS.2018.8639413"},{"issue":"10","key":"533_CR3","doi-asserted-by":"publisher","first-page":"1456","DOI":"10.1109\/5.959341","volume":"89","author":"R Collins","year":"2001","unstructured":"Collins R, Lipton A, Fujiyoshi H, Kanade T (2001) Algorithms for cooperative multisensor surveillance. Proc IEEE 89(10):1456\u20131477. https:\/\/doi.org\/10.1109\/5.959341","journal-title":"Proc IEEE"},{"key":"533_CR4","doi-asserted-by":"publisher","unstructured":"Radke RJ (2009) Chapter 1\u2014multi-view geometry for camera networks. In: Multi-camera networks. Academic Press, Oxford, pp 3\u201327. https:\/\/doi.org\/10.1016\/B978-0-12-374633-7.00003-3","DOI":"10.1016\/B978-0-12-374633-7.00003-3"},{"key":"533_CR5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.patrec.2012.07.005","volume":"34","author":"X Wang","year":"2013","unstructured":"Wang X (2013) Intelligent multi-camera video surveillance: a review. Pattern Recogn Lett 34:3\u201319. https:\/\/doi.org\/10.1016\/j.patrec.2012.07.005","journal-title":"Pattern Recogn Lett"},{"key":"533_CR6","doi-asserted-by":"publisher","unstructured":"Chilgunde A,\u00a0Kumar P,\u00a0Ranganath S,\u00a0WeiMin H Multi-camera target tracking in blind regions of cameras with non-overlapping fields of view. BMVC https:\/\/doi.org\/10.5244\/C.18.42","DOI":"10.5244\/C.18.42"},{"key":"533_CR7","doi-asserted-by":"publisher","unstructured":"Makris D,\u00a0Ellis T (2003) Automatic learning of an activity-based semantic scene model. In: Proceedings of the IEEE conference on advanced video and signal based surveillance, pp 183\u2013188. https:\/\/doi.org\/10.1109\/AVSS.2003.1217920","DOI":"10.1109\/AVSS.2003.1217920"},{"key":"533_CR8","doi-asserted-by":"publisher","unstructured":"Gilbert A,\u00a0Bowden R (2006) Tracking objects across cameras by incrementally learning inter-camera colour calibration and patterns of activity, pp 125\u2013136. https:\/\/doi.org\/10.1007\/11744047_10","DOI":"10.1007\/11744047_10"},{"key":"533_CR9","doi-asserted-by":"publisher","unstructured":"Quanan G, Yunjian X (2020) Kalman filter algorithm for sports video moving target tracking. In: International conference on advance in ambient computing and intelligence (ICAACI), pp 26\u201330. https:\/\/doi.org\/10.1109\/ICAACI50733.2020.00010","DOI":"10.1109\/ICAACI50733.2020.00010"},{"issue":"1","key":"533_CR10","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1007\/s11263-012-0519-6","volume":"99","author":"C-A Deledalle","year":"2012","unstructured":"Deledalle C-A, Denis L, Tupin F (2012) How to compare noisy patches? Patch similarity beyond gaussian noise. Int J Comput Vis 99(1):86\u2013102. https:\/\/doi.org\/10.1007\/s11263-012-0519-6","journal-title":"Int J Comput Vis"},{"key":"533_CR11","doi-asserted-by":"publisher","unstructured":"Yan M,\u00a0Cai J,\u00a0Gao J,\u00a0Luo L (2012) K-means cluster algorithm based on color image enhancement for cell segmentation. In: 2012 5th international conference on biomedical engineering and informatics, pp 295\u2013299. https:\/\/doi.org\/10.1109\/BMEI.2012.6513157","DOI":"10.1109\/BMEI.2012.6513157"},{"key":"533_CR12","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1561\/2000000034","volume":"4","author":"MR Gupta","year":"2011","unstructured":"Gupta MR, Chen Y (2011) Theory and use of the EM algorithm 4:223\u2013296. https:\/\/doi.org\/10.1561\/2000000034","journal-title":"Theory and use of the EM algorithm"},{"key":"533_CR13","doi-asserted-by":"publisher","unstructured":"Lefevre S,\u00a0Bouton E,\u00a0Brouard T,\u00a0Vincent N (2003) A new way to use hidden Markov models for object tracking in video sequences. In: Proceedings 2003 international conference on image processing (Cat. No. 03CH37429), vol\u00a03, pp III\u2013117. https:\/\/doi.org\/10.1109\/ICIP.2003.1247195","DOI":"10.1109\/ICIP.2003.1247195"},{"issue":"6","key":"533_CR14","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1002\/col.5080200605","volume":"20","author":"J Sturges","year":"1995","unstructured":"Sturges J, Whitfield TWA (1995) Locating basic colours in the Munsell space. Color Res Appl 20(6):364\u2013376. https:\/\/doi.org\/10.1002\/col.5080200605","journal-title":"Color Res Appl"},{"key":"533_CR15","unstructured":"Ellis TJ,\u00a0Makris D, Black JK (2003) Learning a multi-camera topology. In: Joint IEEE international workshop on visual surveillance and performance evaluation of tracking and surveillance, pp 165\u2013171"},{"key":"533_CR16","doi-asserted-by":"publisher","unstructured":"Xu M,\u00a0Ellis T (2002) Partial observation vs. blind tracking through occlusion. https:\/\/doi.org\/10.5244\/C.16.76","DOI":"10.5244\/C.16.76"},{"issue":"6","key":"533_CR17","doi-asserted-by":"publisher","first-page":"979","DOI":"10.1109\/TCSVT.2014.2302516","volume":"24","author":"C-T Chu","year":"2014","unstructured":"Chu C-T, Hwang J-N (2014) Fully unsupervised learning of camera link models for tracking humans across nonoverlapping cameras. IEEE Trans Circuits Syst Video Technol 24(6):979\u2013994. https:\/\/doi.org\/10.1109\/TCSVT.2014.2302516","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"533_CR18","doi-asserted-by":"publisher","unstructured":"Lee Y-G, Hwang J.-N,\u00a0Fang Z (2015) Combined estimation of camera link models for human tracking across nonoverlapping cameras. In: 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2254\u20132258. https:\/\/doi.org\/10.1109\/ICASSP.2015.7178372","DOI":"10.1109\/ICASSP.2015.7178372"},{"key":"533_CR19","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1007\/978-3-319-46475-6_30","volume":"2016","author":"C Su","year":"2016","unstructured":"Su C, Zhang S, Xing J, Gao W, Tian Q (2016) Deep attributes driven multi-camera person re-identification. Comput Vis\u2014ECCV 2016:475\u2013491. https:\/\/doi.org\/10.1007\/978-3-319-46475-6_30","journal-title":"Comput Vis\u2014ECCV"},{"issue":"5","key":"533_CR20","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"H-C Shin","year":"2016","unstructured":"Shin H-C, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285\u20131298. https:\/\/doi.org\/10.1109\/TMI.2016.2528162","journal-title":"IEEE Trans Med Imaging"},{"issue":"3","key":"533_CR21","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1007\/s10044-021-00984-y","volume":"24","author":"A Narin","year":"2021","unstructured":"Narin A, Kaya C, Pamuk Z (2021) Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. Pattern Anal Appl 24(3):1207\u20131220. https:\/\/doi.org\/10.1007\/s10044-021-00984-y","journal-title":"Pattern Anal Appl"},{"key":"533_CR22","doi-asserted-by":"publisher","unstructured":"Deng Y,\u00a0Luo P, Loy CC,\u00a0Tang X (2014) Pedestrian attribute recognition at far distance. In: Proceedings of the 22nd ACM international conference on multimedia. ACM. https:\/\/doi.org\/10.1145\/2647868.2654966","DOI":"10.1145\/2647868.2654966"},{"issue":"4","key":"533_CR23","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1007\/s11263-020-01393-0","volume":"129","author":"P Dendorfer","year":"2020","unstructured":"Dendorfer P, Osep A, Milan A, Schindler K, Cremers D, Reid I, Roth S, Leal-Taix\u00e9 L (2020) Motchallenge: a benchmark for single-camera multiple target tracking. Int J Comput Vis 129(4):845\u2013881. https:\/\/doi.org\/10.1007\/s11263-020-01393-0","journal-title":"Int J Comput Vis"},{"key":"533_CR24","unstructured":"Gray D,\u00a0Brennan S,\u00a0Tao H (2007) Evaluating appearance models for recognition, reacquisition, and tracking. In: Proc. IEEE int. workshop vis. surveill. perform. eval. tracking surveill"},{"key":"533_CR25","doi-asserted-by":"publisher","unstructured":"Hirzer M,\u00a0Beleznai C, Roth PM,\u00a0Bischof H (2011) Person re-identification by descriptive and discriminative classification. Springer, Berlin, Heidelberg, pp 91\u2013102. https:\/\/doi.org\/10.1007\/978-3-642-21227-7_9","DOI":"10.1007\/978-3-642-21227-7_9"},{"key":"533_CR26","doi-asserted-by":"publisher","unstructured":"Loy CC, Xiang T, Gong S (2009) Multi-camera activity correlation analysis. In: IEEE conference on computer vision and pattern recognition, pp 1988\u20131995. https:\/\/doi.org\/10.1109\/CVPR.2009.5206827","DOI":"10.1109\/CVPR.2009.5206827"},{"key":"533_CR27","doi-asserted-by":"publisher","unstructured":"Zhang P, Wu Q, Xu J, Zhang J (2018) Long-term person re-identification using true motion from videos. In: IEEE winter conference on applications of computer vision (WACV), pp 494\u2013502. https:\/\/doi.org\/10.1109\/WACV.2018.00060","DOI":"10.1109\/WACV.2018.00060"},{"key":"533_CR28","doi-asserted-by":"publisher","unstructured":"Song Y, Zou JJ,\u00a0Chang H,\u00a0Cai W (2017) Adapting fisher vectors for histopathology image classification. In: 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017), pp 600\u2013603. https:\/\/doi.org\/10.1109\/ISBI.2017.7950592","DOI":"10.1109\/ISBI.2017.7950592"},{"issue":"1","key":"533_CR29","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1007\/s11263-012-0594-8","volume":"103","author":"H Wang","year":"2013","unstructured":"Wang H, Kl\u00e4ser A, Schmid C, Liu C-L (2013) Dense trajectories and motion boundary descriptors for action recognition. Int J Comput Vis 103(1):60\u201379. https:\/\/doi.org\/10.1007\/s11263-012-0594-8","journal-title":"Int J Comput Vis"},{"key":"533_CR30","doi-asserted-by":"publisher","unstructured":"Li W, Zhao R, Xiao T, Wang X (2014) Deepreid: deep filter pairing neural network for person re-identification. In: IEEE conference on computer vision and pattern recognition, pp 152\u2013159. https:\/\/doi.org\/10.1109\/CVPR.2014.27","DOI":"10.1109\/CVPR.2014.27"},{"key":"533_CR31","doi-asserted-by":"publisher","unstructured":"Wang T,\u00a0Gong S,\u00a0Zhu X,\u00a0Wang S (2014) Person re-identification by video ranking. Springer, pp 688\u2013703. https:\/\/doi.org\/10.1007\/978-3-319-10593-2_45","DOI":"10.1007\/978-3-319-10593-2_45"},{"key":"533_CR32","doi-asserted-by":"publisher","unstructured":"Zheng L,\u00a0Bie Z,\u00a0Sun Y,\u00a0Wang J,\u00a0Su C,\u00a0Wang S,\u00a0Tian Q (2016) MARS: a video benchmark for large-scale person re-identification. Springer, pp 868\u2013884. https:\/\/doi.org\/10.1007\/978-3-319-46466-4_52","DOI":"10.1007\/978-3-319-46466-4_52"},{"issue":"9","key":"533_CR33","doi-asserted-by":"publisher","first-page":"2613","DOI":"10.1109\/TCSVT.2017.2736599","volume":"29","author":"N McLaughlin","year":"2019","unstructured":"McLaughlin N, del Rincon JM, Miller P (2019) Video person re-identification for wide area tracking based on recurrent neural networks. IEEE Trans Circuits Syst Video Technol 29(9):2613\u20132626. https:\/\/doi.org\/10.1109\/TCSVT.2017.2736599","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"533_CR34","doi-asserted-by":"publisher","unstructured":"Chauhan R, Ghanshala KK, Joshi R (2018) Convolutional neural network (CNN) for image detection and recognition. In: 1st international conference on secure cyber computing and communication (ICSCCC), pp 278\u2013282. https:\/\/doi.org\/10.1109\/ICSCCC.2018.8703316","DOI":"10.1109\/ICSCCC.2018.8703316"},{"key":"533_CR35","doi-asserted-by":"publisher","unstructured":"Lev G,\u00a0Sadeh G,\u00a0Klein B,\u00a0Wolf L (2016) RNN fisher vectors for action recognition and image annotation. Springer, pp 833\u2013850. https:\/\/doi.org\/10.1007\/978-3-319-46466-4_50","DOI":"10.1007\/978-3-319-46466-4_50"},{"issue":"3","key":"533_CR36","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1023\/b:visi.0000011205.11775.fd","volume":"56","author":"S Baker","year":"2004","unstructured":"Baker S, Matthews I (2004) Lucas-kanade 20 years on: a unifying framework. Int J Comput Vis 56(3):221\u2013255. https:\/\/doi.org\/10.1023\/b:visi.0000011205.11775.fd","journal-title":"Int J Comput Vis"},{"key":"533_CR37","doi-asserted-by":"publisher","unstructured":"Zaheer R,\u00a0Shaziya H (2018) GPU-based empirical evaluation of activation functions in convolutional neural networks. In: 2018 2nd international conference on inventive systems and control (ICISC), pp 769\u2013773. https:\/\/doi.org\/10.1109\/ICISC.2018.8398903","DOI":"10.1109\/ICISC.2018.8398903"},{"key":"533_CR38","doi-asserted-by":"publisher","unstructured":"Teoh SK, Yap VV, Nisar H (2019) A non-overlapping view human tracking algorithm using HSV colour space. In: International conference on green and human information technology (ICGHIT), pp 97\u2013102. https:\/\/doi.org\/10.1109\/ICGHIT.2019.00030","DOI":"10.1109\/ICGHIT.2019.00030"},{"issue":"9","key":"533_CR39","doi-asserted-by":"publisher","first-page":"1777","DOI":"10.1109\/tpami.2014.2382104","volume":"37","author":"IB Ayed","year":"2015","unstructured":"Ayed IB, Punithakumar K, Li S (2015) Distribution matching with the bhattacharyya similarity: a bound optimization framework. IEEE Trans Pattern Anal Mach Intell 37(9):1777\u20131791. https:\/\/doi.org\/10.1109\/tpami.2014.2382104","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"533_CR40","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.cosrev.2014.04.001","volume":"11\u201312","author":"T Bouwmans","year":"2014","unstructured":"Bouwmans T (2014) Traditional and recent approaches in background modeling for foreground detection: an overview. Comput Sci Rev 11\u201312:31\u201366. https:\/\/doi.org\/10.1016\/j.cosrev.2014.04.001","journal-title":"Comput Sci Rev"},{"key":"533_CR41","doi-asserted-by":"publisher","unstructured":"Fomani BA,\u00a0Shahbahrami A (2017) License plate detection using adaptive morphological closing and local adaptive thresholding. In: 2017 3rd international conference on pattern recognition and image analysis (IPRIA), pp 146\u2013150. https:\/\/doi.org\/10.1109\/PRIA.2017.7983035","DOI":"10.1109\/PRIA.2017.7983035"},{"issue":"10","key":"533_CR42","doi-asserted-by":"publisher","first-page":"3028","DOI":"10.1109\/TCSVT.2018.2872957","volume":"29","author":"W Zhang","year":"2019","unstructured":"Zhang W, Li Y, Lu W, Xu X, Liu Z, Ji X (2019) Learning intra-video difference for person re-identification. IEEE Trans Circuits Syst Video Technol 29(10):3028\u20133036. https:\/\/doi.org\/10.1109\/TCSVT.2018.2872957","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"11","key":"533_CR43","doi-asserted-by":"publisher","first-page":"5683","DOI":"10.1109\/TIP.2018.2861366","volume":"27","author":"X Zhu","year":"2018","unstructured":"Zhu X, Jing X-Y, You X, Zhang X, Zhang T (2018) Video-based person re-identification by simultaneously learning intra-video and inter-video distance metrics. IEEE Trans Image Process 27(11):5683\u20135695. https:\/\/doi.org\/10.1109\/TIP.2018.2861366","journal-title":"IEEE Trans Image Process"},{"key":"533_CR44","doi-asserted-by":"publisher","first-page":"156752","DOI":"10.1109\/ACCESS.2019.2950122","volume":"7","author":"A Wu","year":"2019","unstructured":"Wu A, Zheng W-S, Lai J-H (2019) Distilled camera-aware self training for semi-supervised person re-identification. IEEE Access 7:156752\u2013156763. https:\/\/doi.org\/10.1109\/ACCESS.2019.2950122","journal-title":"IEEE Access"},{"issue":"2","key":"533_CR45","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/s10589-009-9283-0","volume":"46","author":"AL Cust\u00f3dio","year":"2009","unstructured":"Cust\u00f3dio AL, Rocha H, Vicente LN (2009) Incorporating minimum Frobenius norm models in direct search. Comput Optim Appl 46(2):265\u2013278. https:\/\/doi.org\/10.1007\/s10589-009-9283-0","journal-title":"Comput Optim Appl"},{"key":"533_CR46","doi-asserted-by":"publisher","unstructured":"Khan K, Rehman SU,\u00a0Aziz K,\u00a0Fong S,\u00a0Sarasvady S (2014) Dbscan: past, present and future. In: The 5th international conference on the applications of digital information and web technologies (ICADIWT 2014), pp 232\u2013238. https:\/\/doi.org\/10.1109\/ICADIWT.2014.6814687","DOI":"10.1109\/ICADIWT.2014.6814687"},{"key":"533_CR47","doi-asserted-by":"publisher","unstructured":"Wei M,\u00a0Pei J (2019) Pedestrian tracking combined with deep learning and camera network topology in non-overlapping multi-camera surveillance. In: 2019 IEEE 14th international conference on intelligent systems and knowledge engineering (ISKE), pp 689\u2013693. https:\/\/doi.org\/10.1109\/ISKE47853.2019.9170386","DOI":"10.1109\/ISKE47853.2019.9170386"},{"key":"533_CR48","doi-asserted-by":"publisher","unstructured":"Lin S, Wong C, Rahman M, Jiang G, Liu S, Kwok N, Shi H, Yu Y-H, Wu T (2015) Image enhancement using the averaging histogram equalization (AVHEQ) approach for contrast improvement and brightness preservation. Comput Electr Eng 46:356\u2013370. https:\/\/doi.org\/10.1016\/j.compeleceng.2015.06.001. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0045790615002050","DOI":"10.1016\/j.compeleceng.2015.06.001"},{"issue":"9","key":"533_CR49","doi-asserted-by":"publisher","first-page":"2815","DOI":"10.1109\/TCSVT.2020.2983600","volume":"30","author":"L Qi","year":"2020","unstructured":"Qi L, Wang L, Huo J, Shi Y, Gao Y (2020) Progressive cross-camera soft-label learning for semi-supervised person re-identification. IEEE Trans Circuits Syst Video Technol 30(9):2815\u20132829. https:\/\/doi.org\/10.1109\/TCSVT.2020.2983600","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"533_CR50","doi-asserted-by":"publisher","unstructured":"Theckedath D, Sedamkar RR (2020) Detecting affect states using vgg16, resnet50 and se-resnet50 networks. SN Comput Sci. https:\/\/doi.org\/10.1007\/s42979-020-0114-9","DOI":"10.1007\/s42979-020-0114-9"},{"key":"533_CR51","doi-asserted-by":"publisher","unstructured":"Sun L,\u00a0Chen Z, Jonathan\u00a0Wu QM,\u00a0Zhao H,\u00a0He W,\u00a0Yan X (2021) Ampnet: average-and max-pool networks for salient object detection. IEEE Trans Circuits Syst Video Technol. https:\/\/doi.org\/10.1109\/TCSVT.2021.3054471","DOI":"10.1109\/TCSVT.2021.3054471"},{"key":"533_CR52","doi-asserted-by":"publisher","unstructured":"Takeda H,\u00a0Yoshida S,\u00a0Muneyasu M (2020) Learning from noisy labeled data using symmetric cross-entropy loss for image classification. In: 2020 IEEE 9th global conference on consumer electronics (GCCE), pp 709\u2013711. https:\/\/doi.org\/10.1109\/GCCE50665.2020.9291873","DOI":"10.1109\/GCCE50665.2020.9291873"},{"key":"533_CR53","unstructured":"Unde AS, Rameshan RM. MOTS R-CNN: cosine-margin-triplet loss for multi-object tracking. arXiv:2102.03512"},{"key":"533_CR54","doi-asserted-by":"crossref","unstructured":"Zheng L,\u00a0Shen L,\u00a0Tian L,\u00a0Wang S,\u00a0Wang J,\u00a0Tian Q (2015) Scalable person re-identification: a benchmark. In: Proceedings of the IEEE international conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2015.133"},{"key":"533_CR55","unstructured":"Zhang Z, Wu J, Zhang X, Zhang C (2017) Multi-Target, Multi-Camera Tracking by Heirarchical Clustering: Recent Progress on DukeMTMC Project. arXiv:abs\/1712.09531"},{"key":"533_CR56","doi-asserted-by":"crossref","unstructured":"Wei L,\u00a0Zhang S,\u00a0Gao W,\u00a0Tian Q (2018) Person transfer gan to bridge domain gap for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2018.00016"},{"key":"533_CR57","doi-asserted-by":"publisher","unstructured":"Plo\u010do A, Rodriguez AM,\u00a0Geradts Z (2020) Spatial-temporal omni-scale feature learning for person re-identification. In: 2020 8th international workshop on biometrics and forensics (IWBF), pp 1\u20135. https:\/\/doi.org\/10.1109\/IWBF49977.2020.9107966","DOI":"10.1109\/IWBF49977.2020.9107966"},{"issue":"2","key":"533_CR58","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1109\/72.207611","volume":"4","author":"P Shi","year":"1993","unstructured":"Shi P, Ward R (1993) Osnet: a neural network implementation of order statistic filters. IEEE Trans Neural Netw 4(2):234\u2013241. https:\/\/doi.org\/10.1109\/72.207611","journal-title":"IEEE Trans Neural Netw"},{"key":"533_CR59","doi-asserted-by":"publisher","unstructured":"Zhou H,\u00a0Kimber D (2006) Unusual event detection via multi-camera video mining. In: 18th international conference on pattern recognition (ICPR\u201906), vol\u00a03, pp 1161\u20131166. https:\/\/doi.org\/10.1109\/ICPR.2006.1149","DOI":"10.1109\/ICPR.2006.1149"},{"key":"533_CR60","doi-asserted-by":"publisher","unstructured":"Yang M-H,\u00a0Ahuja N (1998) Gaussian mixture model for human skin color and its applications in image and video databases. In: Storage and retrieval for image and video databases VII. SPIE. https:\/\/doi.org\/10.1117\/12.333865","DOI":"10.1117\/12.333865"},{"key":"533_CR61","doi-asserted-by":"publisher","unstructured":"Davidson I, Ravi SS (2005) Agglomerative hierarchical clustering with constraints: theoretical and empirical results. Springer, Berlin, Heidelberg, pp 59\u201370. https:\/\/doi.org\/10.1007\/11564126_11","DOI":"10.1007\/11564126_11"},{"key":"533_CR62","doi-asserted-by":"publisher","unstructured":"Brand M,\u00a0Oliver N,\u00a0Pentland A (1997) Coupled hidden Markov models for complex action recognition. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition, pp 994\u2013999. https:\/\/doi.org\/10.1109\/CVPR.1997.609450","DOI":"10.1109\/CVPR.1997.609450"},{"key":"533_CR63","doi-asserted-by":"publisher","unstructured":"Shieh W-Y, Huang J-C (2009) Speedup the multi-camera video-surveillance system for elder falling detection. In: International conference on embedded software and systems, pp 350\u2013355. https:\/\/doi.org\/10.1109\/ICESS.2009.62","DOI":"10.1109\/ICESS.2009.62"},{"issue":"1\u20132","key":"533_CR64","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1080\/08839514.2012.629540","volume":"26","author":"AS Voulodimos","year":"2012","unstructured":"Voulodimos AS, Doulamis ND, Kosmopoulos DI, Varvarigou TA (2012) Improving multi-camera activity recognition by employing neural network based readjustment. Appl Artif Intell 26(1\u20132):97\u2013118. https:\/\/doi.org\/10.1080\/08839514.2012.629540","journal-title":"Appl Artif Intell"},{"issue":"1","key":"533_CR65","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1504\/ijics.2021.115359","volume":"15","author":"JK Bhatia","year":"2021","unstructured":"Bhatia JK, Jalal AS (2021) Pixel-based hybrid copy move image forgery detection using Zernike moments and auto colour correlogram. Int J Inf Comput Secur 15(1):88. https:\/\/doi.org\/10.1504\/ijics.2021.115359","journal-title":"Int J Inf Comput Secur"},{"key":"533_CR66","doi-asserted-by":"publisher","unstructured":"Roizman V,\u00a0Jonckheere M,\u00a0Pascal F (2021) Robust clustering and outlier rejection using the Mahalanobis distance distribution. In: 2020 28th European signal processing conference (EUSIPCO), pp 2448\u20132452. https:\/\/doi.org\/10.23919\/Eusipco47968.2020.9287356","DOI":"10.23919\/Eusipco47968.2020.9287356"},{"key":"533_CR67","doi-asserted-by":"publisher","unstructured":"Rambach J, Huber MF, Balthasar MR, Zoubir AM (2015) Collaborative multi-camera face recognition and tracking. In: 2015 12th IEEE international conference on advanced video and signal based surveillance (AVSS), pp 1\u20136. https:\/\/doi.org\/10.1109\/AVSS.2015.7301765","DOI":"10.1109\/AVSS.2015.7301765"},{"key":"533_CR68","doi-asserted-by":"publisher","unstructured":"Kumar R,\u00a0Rathore H,\u00a0Agrawal P,\u00a0Gupta P (2021) Drowsiness detection using viola-jones object detection algorithm for real-time data. Springer, Singapore, pp 369\u2013380. https:\/\/doi.org\/10.1007\/978-981-16-0171-2_35","DOI":"10.1007\/978-981-16-0171-2_35"},{"key":"533_CR69","doi-asserted-by":"publisher","unstructured":"Nandyal S, Angadi S (2021) Recognition of suspicious human activities using Klt and Kalman filter for ATM surveillance system. In: International conference on innovative practices in technology and management (ICIPTM), pp 174\u2013179. https:\/\/doi.org\/10.1109\/ICIPTM52218.2021.9388322","DOI":"10.1109\/ICIPTM52218.2021.9388322"},{"key":"533_CR70","doi-asserted-by":"publisher","unstructured":"Mohindru G,\u00a0Mondal K,\u00a0Banka H Internet of things and data analytics: a current review. WIREs Data Min Knowl Discov. https:\/\/doi.org\/10.1002\/widm.1341","DOI":"10.1002\/widm.1341"},{"key":"533_CR71","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1016\/j.scs.2017.12.022","volume":"40","author":"MM Rathore","year":"2018","unstructured":"Rathore MM, Paul A, Hong W-H, Seo H, Awan I, Saeed S (2018) Exploiting IoT and big data analytics: defining smart digital city using real-time urban data. Sustain Cities Soc 40:600\u2013610. https:\/\/doi.org\/10.1016\/j.scs.2017.12.022","journal-title":"Sustain Cities Soc"},{"key":"533_CR72","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1016\/j.comnet.2017.06.013","volume":"129","author":"E Ahmed","year":"2017","unstructured":"Ahmed E, Yaqoob I, Hashem IAT, Khan I, Ahmed AIA, Imran M, Vasilakos AV (2017) The role of big data analytics in internet of things. Comput Netw 129:459\u2013471. https:\/\/doi.org\/10.1016\/j.comnet.2017.06.013","journal-title":"Comput Netw"},{"key":"533_CR73","doi-asserted-by":"publisher","unstructured":"Saleem TJ, Chishti MA (2021) Big data analytics for the internet of things. Big Data Analyt Internet Things. https:\/\/doi.org\/10.1002\/9781119740780.ch1","DOI":"10.1002\/9781119740780.ch1"},{"key":"533_CR74","doi-asserted-by":"publisher","unstructured":"Chen N, Chen Y, You Y, Ling H, Liang P, Zimmermann R (2016) Dynamic urban surveillance video stream processing using fog computing. In: IEEE 2nd international conference on multimedia big data (BigMM), pp 105\u2013112. https:\/\/doi.org\/10.1109\/BigMM.2016.53","DOI":"10.1109\/BigMM.2016.53"},{"issue":"5","key":"533_CR75","doi-asserted-by":"publisher","first-page":"2405","DOI":"10.1109\/JSEN.2019.2954331","volume":"20","author":"PG Bhat","year":"2020","unstructured":"Bhat PG, Subudhi BN, Veerakumar T, Laxmi V, Gaur MS (2020) Multi-feature fusion in particle filter framework for visual tracking. IEEE Sens J 20(5):2405\u20132415. https:\/\/doi.org\/10.1109\/JSEN.2019.2954331","journal-title":"IEEE Sens J"},{"issue":"4","key":"533_CR76","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/0005-1098(71)90097-5","volume":"7","author":"H Sorenson","year":"1971","unstructured":"Sorenson H, Alspach D (1971) Recursive Bayesian estimation using gaussian sums. Automatica 7(4):465\u2013479. https:\/\/doi.org\/10.1016\/0005-1098(71)90097-5","journal-title":"Automatica"},{"key":"533_CR77","doi-asserted-by":"publisher","unstructured":"Knowles Z,\u00a0Parker D (2004) A Monte Carlo simulation based approach to a priori performance prediction for target detection and recognition in cluttered synthetic aperture radar imagery. In: IEE target tracking 2004: algorithms and applications, pp 107\u2013114. https:\/\/doi.org\/10.1049\/ic:20040061","DOI":"10.1049\/ic:20040061"},{"key":"533_CR78","doi-asserted-by":"publisher","unstructured":"Yang S-W, Tickoo O, Chen Y-K (2017) A framework for visual fog computing. In: IEEE international symposium on circuits and systems (ISCAS), pp 1\u20134. https:\/\/doi.org\/10.1109\/ISCAS.2017.8050297","DOI":"10.1109\/ISCAS.2017.8050297"},{"key":"533_CR79","doi-asserted-by":"publisher","unstructured":"Kioumourtzis G,\u00a0Skitsas M,\u00a0Zotos N,\u00a0Sideris A (2017) Wide area video surveillane based on edge and fog computing concept. In: 2017 8th international conference on information, intelligence, systems applications (IISA), pp 1\u20136. https:\/\/doi.org\/10.1109\/IISA.2017.8316451","DOI":"10.1109\/IISA.2017.8316451"},{"key":"533_CR80","doi-asserted-by":"publisher","unstructured":"Camboim HB, Neto AJV, Rodrigues AJV,\u00a0Zhao Z (2017) Applying fog computing to improve crime assistance in smart transportation safety systems. In: 2017 IEEE 1st summer school on smart cities (S3C), pp 19\u201324. https:\/\/doi.org\/10.1109\/S3C.2017.8501398","DOI":"10.1109\/S3C.2017.8501398"},{"key":"533_CR81","doi-asserted-by":"publisher","unstructured":"Zhang J,\u00a0Li S,\u00a0Wang Y Shaping a smart transportation system for sustainable value co-creation. Inf Syst Front. https:\/\/doi.org\/10.1007\/s10796-021-10139-3","DOI":"10.1007\/s10796-021-10139-3"},{"key":"533_CR82","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.jpdc.2018.11.004","volume":"126","author":"M Nasir","year":"2019","unstructured":"Nasir M, Muhammad K, Lloret J, Sangaiah AK, Sajjad M (2019) Fog computing enabled cost-effective distributed summarization of surveillance videos for smart cities. J Parallel Distrib Comput 126:161\u2013170. https:\/\/doi.org\/10.1016\/j.jpdc.2018.11.004","journal-title":"J Parallel Distrib Comput"},{"key":"533_CR83","doi-asserted-by":"publisher","first-page":"175","DOI":"10.12720\/jcm.16.5.175-184","volume":"16","author":"A Mosaif","year":"2021","unstructured":"Mosaif A, Rakrak S (2021) A new system for real-time video surveillance in smart cities based on wireless visual sensor networks and fog computing. J Commun 16:175\u2013184. https:\/\/doi.org\/10.12720\/jcm.16.5.175-184","journal-title":"J Commun"},{"key":"533_CR84","doi-asserted-by":"publisher","unstructured":"Perala SSN, Galanis I, Anagnostopoulos I (2018) Fog computing and efficient resource management in the era of internet-of-video things (IOVT). In: IEEE international symposium on circuits and systems (ISCAS), pp 1\u20135. https:\/\/doi.org\/10.1109\/ISCAS.2018.8351341","DOI":"10.1109\/ISCAS.2018.8351341"},{"key":"533_CR85","doi-asserted-by":"publisher","unstructured":"Ledakis I,\u00a0Bouras T,\u00a0Kioumourtzis G,\u00a0Skitsas M (2018) Adaptive edge and fog computing paradigm for wide area video and audio surveillance. In: 2018 9th international conference on information, intelligence, systems and applications (IISA), pp 1\u20135. https:\/\/doi.org\/10.1109\/IISA.2018.8633626","DOI":"10.1109\/IISA.2018.8633626"},{"key":"533_CR86","doi-asserted-by":"publisher","unstructured":"Chen N, Chen Y, Blasch E, Ling H, You Y, Ye X (2017) Enabling smart urban surveillance at the edge. In: IEEE international conference on smart cloud (smartCloud), pp 109\u2013119. https:\/\/doi.org\/10.1109\/SmartCloud.2017.24","DOI":"10.1109\/SmartCloud.2017.24"},{"key":"533_CR87","doi-asserted-by":"publisher","first-page":"134881","DOI":"10.1109\/ACCESS.2019.2941978","volume":"7","author":"T Sultana","year":"2019","unstructured":"Sultana T, Wahid KA (2019) Iot-guard: event-driven fog-based video surveillance system for real-time security management. IEEE Access 7:134881\u2013134894. https:\/\/doi.org\/10.1109\/ACCESS.2019.2941978","journal-title":"IEEE Access"},{"key":"533_CR88","doi-asserted-by":"publisher","unstructured":"Taheri\u00a0Tajar A,\u00a0Ramazani A,\u00a0Mansoorizadeh M (2021) A lightweight tiny-yolov3 vehicle detection approach. J Real-Time Image Process. https:\/\/doi.org\/10.1007\/s11554-021-01131-w","DOI":"10.1007\/s11554-021-01131-w"},{"key":"533_CR89","doi-asserted-by":"publisher","unstructured":"Muniswamaiah M,\u00a0Agerwala T, Tappert CC (2021) Fog computing and the internet of things (IoT): a review. In: 2021 8th IEEE international conference on cyber security and cloud computing (CSCloud)\/2021 7th IEEE international conference on edge computing and scalable cloud (EdgeCom), pp 10\u201312. https:\/\/doi.org\/10.1109\/CSCloud-EdgeCom52276.2021.00012","DOI":"10.1109\/CSCloud-EdgeCom52276.2021.00012"},{"key":"533_CR90","doi-asserted-by":"publisher","unstructured":"Peralta G, Iglesias-Urkia M, Barcelo M, Gomez R, Moran A, Bilbao J (2017) Fog computing based efficient IoT scheme for the industry 4.0. In: IEEE international workshop of electronics, control, measurement. signals and their application to mechatronics (ECMSM), pp 1\u20136. https:\/\/doi.org\/10.1109\/ECMSM.2017.7945879","DOI":"10.1109\/ECMSM.2017.7945879"},{"issue":"5","key":"533_CR91","doi-asserted-by":"publisher","first-page":"1728","DOI":"10.1109\/JSAC.2016.2545559","volume":"34","author":"F Jalali","year":"2016","unstructured":"Jalali F, Hinton K, Ayre R, Alpcan T, Tucker RS (2016) Fog computing may help to save energy in cloud computing. IEEE J Sel Areas Commun 34(5):1728\u20131739. https:\/\/doi.org\/10.1109\/JSAC.2016.2545559","journal-title":"IEEE J Sel Areas Commun"},{"issue":"12","key":"533_CR92","doi-asserted-by":"publisher","first-page":"9634","DOI":"10.1109\/JIOT.2020.3027483","volume":"8","author":"T Hussain","year":"2021","unstructured":"Hussain T, Muhammad K, Ullah A, Ser JD, Gandomi AH, Sajjad M, Baik SW, de Albuquerque VHC (2021) Multiview summarization and activity recognition meet edge computing in IoT environments. IEEE Internet Things J 8(12):9634\u20139644. https:\/\/doi.org\/10.1109\/JIOT.2020.3027483","journal-title":"IEEE Internet Things J"},{"key":"533_CR93","doi-asserted-by":"publisher","first-page":"199829","DOI":"10.1109\/ACCESS.2020.3035181","volume":"8","author":"AU Rehman","year":"2020","unstructured":"Rehman AU, Ahmad Z, Jehangiri AI, Ala\u2019Anzy MA, Othman M, Umar AI, Ahmad J (2020) Dynamic energy efficient resource allocation strategy for load balancing in fog environment. IEEE Access 8:199829\u2013199839. https:\/\/doi.org\/10.1109\/ACCESS.2020.3035181","journal-title":"IEEE Access"},{"key":"533_CR94","doi-asserted-by":"publisher","DOI":"10.1016\/j.suscom.2019.100355","volume":"24","author":"S Sharma","year":"2019","unstructured":"Sharma S, Saini H (2019) A novel four-tier architecture for delay aware scheduling and load balancing in fog environment. Sustain Comput: Informat Syst 24:100355. https:\/\/doi.org\/10.1016\/j.suscom.2019.100355","journal-title":"Sustain Comput: Informat Syst"},{"issue":"8","key":"533_CR95","doi-asserted-by":"publisher","first-page":"9202","DOI":"10.1007\/s11227-020-03600-8","volume":"77","author":"M Kaur","year":"2021","unstructured":"Kaur M, Aron R (2021) A systematic study of load balancing approaches in the fog computing environment. J Supercomput 77(8):9202\u20139247. https:\/\/doi.org\/10.1007\/s11227-020-03600-8","journal-title":"J Supercomput"},{"key":"533_CR96","doi-asserted-by":"publisher","unstructured":"Kaarmukilan SP, Hazarika A, Poddar S, Rahaman H (2020) An accelerated prototype with movidius neural compute stick for real-time object detection. In: International symposium on devices. Circuits and systems (ISDCS), pp 1\u20135. https:\/\/doi.org\/10.1109\/ISDCS49393.2020.9262996","DOI":"10.1109\/ISDCS49393.2020.9262996"},{"key":"533_CR97","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/7218758","volume":"2019","author":"G Dinelli","year":"2019","unstructured":"Dinelli G, Meoni G, Rapuano E, Benelli G, Fanucci L (2019) An FPGA-based hardware accelerator for CNNs using on-chip memories only: design and benchmarking with intel movidius neural compute stick. Int J Reconfig Comput 2019:1\u201313. https:\/\/doi.org\/10.1155\/2019\/7218758","journal-title":"Int J Reconfig Comput"},{"key":"533_CR98","doi-asserted-by":"publisher","unstructured":"Xu X,\u00a0Amaro J,\u00a0Caulfield S,\u00a0Forembski A,\u00a0Falcao G,\u00a0Moloney D (2017) Convolutional neural network on neural compute stick for voxelized point-clouds classification. In: 2017 10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), pp 1\u20137. https:\/\/doi.org\/10.1109\/CISP-BMEI.2017.8302078","DOI":"10.1109\/CISP-BMEI.2017.8302078"},{"key":"533_CR99","doi-asserted-by":"publisher","unstructured":"Li Q, Song J, Ning J, Yuan J (2019) The detailed data on the neural compute stick acceleration performance. In: Chinese automation congress (CAC), pp 4959\u20134962. https:\/\/doi.org\/10.1109\/CAC48633.2019.8996841","DOI":"10.1109\/CAC48633.2019.8996841"},{"key":"533_CR100","doi-asserted-by":"publisher","unstructured":"Tiwari N, Mondal K (2019) Ncs based ultra low power optimized machine learning techniques for image classification. In: IEEE region 10 symposium (TENSYMP). IEEE. https:\/\/doi.org\/10.1109\/tensymp46218.2019.8971238","DOI":"10.1109\/tensymp46218.2019.8971238"},{"issue":"11","key":"533_CR101","doi-asserted-by":"publisher","first-page":"243","DOI":"10.14257\/ijsip.2015.8.11.22","volume":"8","author":"X Li","year":"2015","unstructured":"Li X, Dong W, Chang F, Qu P (2015) Topology learning of non-overlapping multi-camera network. Int J Signal Process, Image Process Pattern Recogn 8(11):243\u2013254. https:\/\/doi.org\/10.14257\/ijsip.2015.8.11.22","journal-title":"Int J Signal Process, Image Process Pattern Recogn"},{"key":"533_CR102","doi-asserted-by":"publisher","unstructured":"Nikouei SY, Chen Y, Song S, Xu R, Choi B-Y, Faughnan TR (2018) Real-time human detection as an edge service enabled by a lightweight CNN. In: IEEE international conference on edge computing (EDGE), pp 125\u2013129. https:\/\/doi.org\/10.1109\/EDGE.2018.00025","DOI":"10.1109\/EDGE.2018.00025"},{"issue":"3","key":"533_CR103","doi-asserted-by":"publisher","first-page":"3981","DOI":"10.1007\/s11042-020-09749-x","volume":"80","author":"S Jha","year":"2020","unstructured":"Jha S, Seo C, Yang E, Joshi GP (2020) Real time object detection and tracking system for video surveillance system. Multimedia Tools Appl 80(3):3981\u20133996. https:\/\/doi.org\/10.1007\/s11042-020-09749-x","journal-title":"Multimedia Tools Appl"}],"container-title":["Innovations in Systems and Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11334-023-00533-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11334-023-00533-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11334-023-00533-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,8]],"date-time":"2025-03-08T02:41:03Z","timestamp":1741401663000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11334-023-00533-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,24]]},"references-count":103,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["533"],"URL":"https:\/\/doi.org\/10.1007\/s11334-023-00533-2","relation":{},"ISSN":["1614-5046","1614-5054"],"issn-type":[{"value":"1614-5046","type":"print"},{"value":"1614-5054","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,24]]},"assertion":[{"value":"1 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}