{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T23:37:29Z","timestamp":1788997049169,"version":"build-2803163510"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T00:00:00Z","timestamp":1642464000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T00:00:00Z","timestamp":1642464000000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-021-11891-z","type":"journal-article","created":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T15:05:15Z","timestamp":1642518315000},"page":"15339-15361","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Tracking subjects and detecting relationships in crowded city videos"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4558-6802","authenticated-orcid":false,"given":"Petr","family":"Elias","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matus","family":"Macko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan","family":"Sedmidubsky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pavel","family":"Zezula","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,18]]},"reference":[{"key":"11891_CR1","unstructured":"Babaee M, Athar A, Rigoll G (2018) Multiple people tracking using hierarchical deep tracklet re-identification. arXiv:1811.04091"},{"key":"11891_CR2","doi-asserted-by":"crossref","unstructured":"Bera A, Randhavane T, Kubin E, Shaik H, Gray K, Manocha D (2018) Data-driven modeling of group entitativity in virtual environments. In: Spencer SN, Morishima S, Itoh Y, Shiratori T, Yue Y, Lindeman R (eds) Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology, VRST 2018. ACM, Tokyo, pp 31:1\u201331:10","DOI":"10.1145\/3281505.3281524"},{"key":"11891_CR3","doi-asserted-by":"crossref","unstructured":"Bergmann P, Meinhardt T, Leal-taix\u0117 L (2019) Tracking without bells and whistles. In: Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019. IEEE, Seoul, pp 941\u2013951","DOI":"10.1109\/ICCV.2019.00103"},{"key":"11891_CR4","doi-asserted-by":"crossref","unstructured":"Bernardin K, Stiefelhagen R (2008) Evaluating multiple object tracking performance: The CLEAR MOT metrics. EURASIP J Image Video Process","DOI":"10.1155\/2008\/246309"},{"key":"11891_CR5","doi-asserted-by":"crossref","unstructured":"Bewley A, Ge Z, Ott L, Ramos FT, Upcroft B (2016) Simple online and realtime tracking. In: 2016 IEEE International conference on image processing, ICIP 2016. IEEE, Phoenix, pp 3464\u20133468","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"11891_CR6","doi-asserted-by":"crossref","unstructured":"Bochinski E, Eiselein V, Sikora T (2017) High-speed tracking-by-detection without using image information. In: 14Th IEEE international conference on advanced video and signal based surveillance, AVSS 2017. IEEE Computer Society, Lecce, pp 1\u20136","DOI":"10.1109\/AVSS.2017.8078516"},{"key":"11891_CR7","doi-asserted-by":"crossref","unstructured":"Braso\u0307 G., Leal-taix\u0117 L (2020) Learning a neural solver for multiple object tracking. In: International conference on computer vision and pattern recognition (CVPR). IEEE, pp 6246\u20136256","DOI":"10.1109\/CVPR42600.2020.00628"},{"key":"11891_CR8","doi-asserted-by":"crossref","unstructured":"Cao Z, Simon T, Wei S, Sheikh Y (2017) Realtime multi-person 2d pose estimation using part affinity fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1302\u20131310","DOI":"10.1109\/CVPR.2017.143"},{"issue":"19","key":"11891_CR9","doi-asserted-by":"publisher","first-page":"27,309","DOI":"10.1007\/s11042-019-07827-3","volume":"78","author":"F Carrara","year":"2019","unstructured":"Carrara F, Elias P, Sedmidubsky J, Zezula P (2019) Lstm-based real-time action detection and prediction in human motion streams. Multimed Tools Appl 78(19):27,309\u201327,331","journal-title":"Multimed Tools Appl"},{"issue":"11","key":"11891_CR10","doi-asserted-by":"publisher","first-page":"1613","DOI":"10.1109\/LSP.2019.2940922","volume":"26","author":"L Chen","year":"2019","unstructured":"Chen L, Ai H, Chen R, Zhuang Z (2019) Aggregate tracklet appearance features for multi-object tracking. IEEE Signal Process Lett 26(11):1613\u20131617","journal-title":"IEEE Signal Process Lett"},{"key":"11891_CR11","doi-asserted-by":"crossref","unstructured":"Choi W, Savarese S (2012) A unified framework for multi-target tracking and collective activity recognition. In: Fitzgibbon AW, Lazebnik S, Perona P, Sato Y, Schmid C (eds) Proceedings, Part IV, Lecture Notes in Computer Science, vol 7575. Springer, Florence, pp 215\u2013230","DOI":"10.1007\/978-3-642-33765-9_16"},{"key":"11891_CR12","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1109\/ACCESS.2019.2961778","volume":"8","author":"J Chu","year":"2020","unstructured":"Chu J, Tu X, Leng L, Miao J (2020) Double-channel object tracking with position deviation suppression. IEEE Access 8:856\u2013866","journal-title":"IEEE Access"},{"key":"11891_CR13","doi-asserted-by":"crossref","unstructured":"Chu P, Ling H (2019) Famnet: Joint learning of feature, affinity and multi-dimensional assignment for online multiple object tracking. In: 2019 IEEE\/CVF International conference on computer vision, ICCV 2019. IEEE, Seoul, pp 6171\u20136180","DOI":"10.1109\/ICCV.2019.00627"},{"issue":"10","key":"11891_CR14","doi-asserted-by":"publisher","first-page":"2941","DOI":"10.1109\/TCSVT.2018.2870832","volume":"29","author":"R Cong","year":"2019","unstructured":"Cong R, Lei J, Fu H, Cheng M, Lin W, Huang Q (2019) Review of visual saliency detection with comprehensive information. IEEE Trans Circ Syst Video Technol 29(10):2941\u20132959","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"11891_CR15","doi-asserted-by":"crossref","unstructured":"Cristani M, Bazzani L, Paggetti G, Fossati A, Tosato D, Bue AD, Menegaz G, Murino V (2011) Social interaction discovery by statistical analysis of f-formations. In: British machine vision conference, BMVC 2011. Proceedings. BMVA Press, Dundee, pp 1\u201312","DOI":"10.5244\/C.25.23"},{"issue":"4","key":"11891_CR16","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1093\/genetics\/2.4.313","volume":"2","author":"CB Davenport","year":"1917","unstructured":"Davenport CB (1917) Inheritance of stature. Genetics 2(4):313\u2013389","journal-title":"Genetics"},{"key":"11891_CR17","unstructured":"Dendorfer P, Rezatofighi H, Milan A, Shi J, Cremers D, Reid I, Roth S, Schindler K, Leal-Taix\u00e9 L. (2003) Mot20: A benchmark for multi object tracking in crowded scenes. arXiv:2003.09003[cs]. arXiv:1906.04567"},{"issue":"10","key":"11891_CR18","doi-asserted-by":"publisher","first-page":"1858","DOI":"10.1109\/TPAMI.2008.113","volume":"30","author":"GD Evangelidis","year":"2008","unstructured":"Evangelidis GD, Psarakis EZ (2008) Parametric image alignment using enhanced correlation coefficient maximization. IEEE Trans Pattern Anal Mach Intell 30(10):1858\u20131865","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11891_CR19","doi-asserted-by":"crossref","unstructured":"Fan D, Wang W, Cheng M, Shen J (2019) Shifting more attention to video salient object detection. In: IEEE Conference on computer vision and pattern recognition, CVPR 2019. Computer Vision Foundation \/ IEEE, Long Beach, pp 8554\u20138564","DOI":"10.1109\/CVPR.2019.00875"},{"issue":"9","key":"11891_CR20","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2010","unstructured":"Felzenszwalb PF, Girshick RB, McAllester DA, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627\u20131645","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11891_CR21","unstructured":"Feng W, Hu Z, Wu W, Yan J, Ouyang W (2019) Multi-object tracking with multiple cues and switcher-aware classification. arXiv:1901.06129"},{"key":"11891_CR22","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.neucom.2019.04.062","volume":"356","author":"K Fu","year":"2019","unstructured":"Fu K, Zhao Q, Gu IY, Yang J (2019) Deepside: A general deep framework for salient object detection. Neurocomputing 356:69\u201382","journal-title":"Neurocomputing"},{"key":"11891_CR23","doi-asserted-by":"crossref","unstructured":"Henschel R, Leal-taix\u0117 L, Cremers D, Rosenhahn B (2018) Fusion of head and full-body detectors for multi-object tracking. In: 2018 IEEE Conference on computer vision and pattern recognition workshops, CVPR workshops 2018. IEEE Computer Society, Salt Lake City, pp 1428\u20131437","DOI":"10.1109\/CVPRW.2018.00192"},{"key":"11891_CR24","doi-asserted-by":"crossref","unstructured":"Henschel R, Zou Y, Rosenhahn B (2019) Multiple people tracking using body and joint detections. In: IEEE Conference on computer vision and pattern recognition workshops, CVPR workshops 2019. Computer Vision Foundation \/ IEEE, Long Beach, p 0","DOI":"10.1109\/CVPRW.2019.00105"},{"issue":"20","key":"11891_CR25","doi-asserted-by":"publisher","first-page":"28,715","DOI":"10.1007\/s11042-018-6074-6","volume":"78","author":"T Hu","year":"2019","unstructured":"Hu T, Zhu X, Wang S, Duan L (2019) Human interaction recognition using spatial-temporal salient feature. Multimedia Tools and Applications 78(20):28,715\u201328,735","journal-title":"Multimedia Tools and Applications"},{"key":"11891_CR26","doi-asserted-by":"crossref","unstructured":"Insafutdinov E, Andriluka M, Pishchulin L, Tang S, Levinkov E, Andres B, Schiele B (2017) Arttrack: Articulated multi-person tracking in the wild. In: 2017 IEEE Conference on computer vision and pattern recognition, CVPR 2017. IEEE Computer Society, Honolulu, pp 1293\u20131301","DOI":"10.1109\/CVPR.2017.142"},{"key":"11891_CR27","doi-asserted-by":"crossref","unstructured":"Iqbal U, Milan A, Gall J (2017) Posetrack: Joint multi-person pose estimation and tracking. In: 2017 IEEE Conference on computer vision and pattern recognition, CVPR 2017. IEEE Computer Society, Honolulu, pp 4654\u20134663","DOI":"10.1109\/CVPR.2017.495"},{"issue":"4","key":"11891_CR28","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/BF02278710","volume":"38","author":"R Jonker","year":"1987","unstructured":"Jonker R, Volgenant A (1987) A shortest augmenting path algorithm for dense and sparse linear assignment problems. Computing 38(4):325\u2013340","journal-title":"Computing"},{"issue":"1","key":"11891_CR29","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1115\/1.3662552","volume":"82","author":"RE Kalman","year":"1960","unstructured":"Kalman RE (1960) A New Approach to Linear Filtering and Prediction Problems. J Basic Eng 82(1):35\u201345","journal-title":"J Basic Eng"},{"key":"11891_CR30","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1016\/j.ins.2014.03.080","volume":"278","author":"DY Kim","year":"2014","unstructured":"Kim DY, Jeon M (2014) Data fusion of radar and image measurements for multi-object tracking via kalman filtering. Inf Sci 278:641\u2013652. https:\/\/doi.org\/10.1016\/j.ins.2014.03.080","journal-title":"Inf Sci"},{"key":"11891_CR31","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1016\/j.neucom.2015.11.021","volume":"177","author":"VJ Kok","year":"2016","unstructured":"Kok VJ, Lim MK, Chan CS (2016) Crowd behavior analysis: A review where physics meets biology. Neurocomputing 177:342\u2013362","journal-title":"Neurocomputing"},{"issue":"3","key":"11891_CR32","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.cviu.2011.08.006","volume":"116","author":"B Krausz","year":"2012","unstructured":"Krausz B, Bauckhage C (2012) Loveparade 2010: Automatic video analysis of a crowd disaster. Comput Vis Image Underst 116(3):307\u2013319","journal-title":"Comput Vis Image Underst"},{"key":"11891_CR33","doi-asserted-by":"crossref","unstructured":"Leal-Taix\u0117 L, Pons-Moll G, Rosenhahn B (2011) Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker. In: IEEE International conference on computer vision workshops, ICCV 2011 workshops. IEEE computer society, Barcelona, pp 120\u2013127","DOI":"10.1109\/ICCVW.2011.6130233"},{"issue":"4","key":"11891_CR34","doi-asserted-by":"publisher","first-page":"1834","DOI":"10.1109\/TIP.2015.2510583","volume":"25","author":"H Li","year":"2016","unstructured":"Li H, Li Y, Porikli F (2016) Deeptrack: Learning discriminative feature representations online for robust visual tracking. IEEE Trans Image Process 25(4):1834\u20131848. https:\/\/doi.org\/10.1109\/TIP.2015.2510583","journal-title":"IEEE Trans Image Process"},{"issue":"3","key":"11891_CR35","doi-asserted-by":"publisher","first-page":"2966","DOI":"10.1109\/JSYST.2018.2880028","volume":"13","author":"K Li","year":"2019","unstructured":"Li K, Yuen C, Kanhere SS, Hu K, Zhang W, Jiang F, Liu X (2019) An experimental study for tracking crowd in smart cities. IEEE Syst J 13 (3):2966\u20132977","journal-title":"IEEE Syst J"},{"key":"11891_CR36","doi-asserted-by":"publisher","first-page":"122, 772","DOI":"10.1109\/ACCESS.2020.3007261","volume":"8","author":"S Li","year":"2020","unstructured":"Li S, Chu J, Zhong G, Leng L, Miao J (2020) Robust visual tracking with occlusion judgment and re-detection. IEEE Access 8:122, 772\u2013122, 781","journal-title":"IEEE Access"},{"key":"11891_CR37","doi-asserted-by":"crossref","unstructured":"Li Y, Huang C, Nevatia R (2009) Learning to associate: Hybridboosted multi-target tracker for crowded scene. In: 2009 IEEE Computer society conference on computer vision and pattern recognition (CVPR 2009). IEEE Computer Society, Miami, pp 2953\u20132960","DOI":"10.1109\/CVPR.2009.5206735"},{"key":"11891_CR38","doi-asserted-by":"crossref","unstructured":"Liciotti D, Contigiani M, Frontoni E, Mancini A, Zingaretti P, Placidi V (2014) Shopper analytics: A customer activity recognition system using a distributed RGB-d camera network. In: Video analytics for audience measurement - first international workshop, VAAM 2014. Revised selected papers, lecture notes in computer science, vol 8811. Springer, Stockholm, pp 146\u2013157","DOI":"10.1007\/978-3-319-12811-5_11"},{"key":"11891_CR39","doi-asserted-by":"crossref","unstructured":"Lu X, Ma C, Ni B, Yang X, Reid ID, Yang M (2018) Deep regression tracking with shrinkage loss. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer Vision - ECCV 2018 - 15th European Conference, Proceedings, Part XIV, Lecture Notes in Computer Science, vol 11218. Springer, Munich, pp 369\u2013386","DOI":"10.1007\/978-3-030-01264-9_22"},{"issue":"6","key":"11891_CR40","doi-asserted-by":"publisher","first-page":"7077","DOI":"10.1007\/s11042-018-6467-6","volume":"78","author":"N Mahmoudi","year":"2019","unstructured":"Mahmoudi N, Ahadi SM, Rahmati M (2019) Multi-target tracking using cnn-based features: CNNMTT. Multimed Tools Appl 78(6):7077\u20137096","journal-title":"Multimed Tools Appl"},{"key":"11891_CR41","doi-asserted-by":"crossref","unstructured":"Mehmood R, Katib SSI, Chlamtac I (2020) Smart infrastructure and applications. Springer","DOI":"10.1007\/978-3-030-13705-2"},{"key":"11891_CR42","unstructured":"Milan A, Leal-Taix\u00e9 L, Reid ID, Roth S, Schindler K (2016) MOT16: A benchmark for multi-object tracking. arXiv:1603.00831"},{"issue":"1","key":"11891_CR43","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1137\/0105003","volume":"5","author":"J Munkres","year":"1957","unstructured":"Munkres J (1957) Algorithms for the assignment and transportation problems. J Soc Indust Appl Math 5(1):32\u201338","journal-title":"J Soc Indust Appl Math"},{"key":"11891_CR44","doi-asserted-by":"crossref","unstructured":"Pan G, Qi G, Zhang W, Li S, Wu Z, Yang LT (2013) Trace analysis and mining for smart cities: issues, methods, and applications. IEEE Commun Mag 51(6)","DOI":"10.1109\/MCOM.2013.6525604"},{"key":"11891_CR45","doi-asserted-by":"crossref","unstructured":"Pirsiavash H, Ramanan D, Fowlkes CC (2011) Globally-optimal greedy algorithms for tracking a variable number of objects. In: The 24th IEEE conference on computer vision and pattern recognition, CVPR 2011. IEEE Computer Society, Colorado Springs, pp 1201\u20131208","DOI":"10.1109\/CVPR.2011.5995604"},{"key":"11891_CR46","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 6517\u20136525","DOI":"10.1109\/CVPR.2017.690"},{"issue":"6","key":"11891_CR47","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick RB, Sun J (2017) Faster r-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"11891_CR48","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/s00530-017-0558-5","volume":"25","author":"JJ Salmero\u0307n-Garc\u00eda","year":"2019","unstructured":"Salmero\u0307n-Garc\u00eda JJ, Van den Dries S, Del R\u00edo FD, Est\u0117vez AM, Sevillano-Ramos JL, Van de Molengraft MJG (2019) Towards a cloud-based automated surveillance system using wireless technologies. Multimed Syst 25(5):535\u2013549","journal-title":"Multimed Syst"},{"issue":"10","key":"11891_CR49","doi-asserted-by":"publisher","first-page":"12, 073","DOI":"10.1007\/s11042-017-4859-7","volume":"77","author":"J Sedmidubsky\u0307","year":"2018","unstructured":"Sedmidubsky\u0307 J, Elias P, Zezula P (2018) Effective and efficient similarity searching in motion capture data. Multimed Tools Appl 77 (10):12, 073\u201312,094","journal-title":"Multimed Tools Appl"},{"key":"11891_CR50","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.is.2018.04.002","volume":"80","author":"J Sedmidubsky","year":"2019","unstructured":"Sedmidubsky J, Elias P, Zezula P (2019) Searching for variable-speed motions in long sequences of motion capture data. Inf Syst 80:148\u2013158","journal-title":"Inf Syst"},{"issue":"11","key":"11891_CR51","doi-asserted-by":"publisher","first-page":"3269","DOI":"10.1109\/TCSVT.2018.2882192","volume":"29","author":"H Sheng","year":"2019","unstructured":"Sheng H, Zhang Y, Chen J, Xiong Z, Zhang J (2019) Heterogeneous association graph fusion for target association in multiple object tracking. IEEE Trans Circ Syst Video Techn 29(11):3269\u20133280","journal-title":"IEEE Trans Circ Syst Video Techn"},{"issue":"1","key":"11891_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/0925-7535(96)81011-3","volume":"21","author":"JD Sime","year":"1995","unstructured":"Sime JD (1995) Crowd psychology and engineering. Safety Sci 21 (1):1\u201314","journal-title":"Safety Sci"},{"key":"11891_CR53","doi-asserted-by":"crossref","unstructured":"Sun K, Xiao B, Liu D, Wang J (2019) Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 5693\u20135703","DOI":"10.1109\/CVPR.2019.00584"},{"key":"11891_CR54","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.patrec.2013.09.015","volume":"44","author":"KN Tran","year":"2014","unstructured":"Tran KN, Gala A, Kakadiaris IA, Shah SK (2014) Activity analysis in crowded environments using social cues for group discovery and human interaction modeling. Pattern Recognit Lett 44:49\u201357","journal-title":"Pattern Recognit Lett"},{"key":"11891_CR55","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.cviu.2015.09.012","volume":"143","author":"S Vascon","year":"2016","unstructured":"Vascon S, Mequanint EZ, Cristani M, Hung H, Pelillo M, Murino V (2016) Detecting conversational groups in images and sequences: A robust game-theoretic approach. Comput Vis Image Underst 143:11\u201324","journal-title":"Comput Vis Image Underst"},{"key":"11891_CR56","doi-asserted-by":"crossref","unstructured":"Wang G, Wang Y, Zhang H, Gu R, Hwang J (2019) Exploit the connectivity: Multi-object tracking with trackletnet. In: Amsaleg L, Huet B, Larson MA, Gravier G, Hung H, Ngo C, Ooi WT (eds) Proceedings of the 27th ACM International Conference on Multimedia, MM 2019. ACM, Nice, pp 482\u2013490","DOI":"10.1145\/3343031.3350853"},{"key":"11891_CR57","doi-asserted-by":"crossref","unstructured":"Wojke N, Bewley A, Paulus D (2017) Simple online and realtime tracking with a deep association metric. In: 2017 IEEE International conference on image processing, ICIP 2017. IEEE, Beijing, pp 3645\u20133649","DOI":"10.1109\/ICIP.2017.8296962"},{"issue":"3","key":"11891_CR58","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1109\/THMS.2017.2776211","volume":"48","author":"H Wu","year":"2018","unstructured":"Wu H, Shao J, Xu X, Ji Y, Shen F, Shen HT (2018) Recognition and detection of two-person interactive actions using automatically selected skeleton features. IEEE Trans Hum Mach Syst 48(3):304\u2013310","journal-title":"IEEE Trans Hum Mach Syst"},{"key":"11891_CR59","doi-asserted-by":"crossref","unstructured":"Xu R, Nikouei SY, Chen Y, Polunchenko A, Song S, Deng C, Faughnan TR (2018) Real-time human objects tracking for smart surveillance at the edge. In: 2018 IEEE International conference on communications, ICC 2018. IEEE, Kansas City, pp 1\u20136","DOI":"10.1109\/ICC.2018.8422970"},{"key":"11891_CR60","unstructured":"Yang B, Nevatia R (2012) An online learned CRF model for multi-target tracking. In: 2012 IEEE Conference on computer vision and pattern recognition. IEEE Computer Society, Providence, pp 2034\u20132041"},{"key":"11891_CR61","doi-asserted-by":"crossref","unstructured":"Yang F, Choi W, Lin Y (2016) Exploit all the layers: Fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016. IEEE Computer Society, Las Vegas, pp 2129\u20132137","DOI":"10.1109\/CVPR.2016.234"},{"issue":"10","key":"11891_CR62","doi-asserted-by":"publisher","first-page":"2988","DOI":"10.3390\/s20102988","volume":"20","author":"T Yigitcanlar","year":"2020","unstructured":"Yigitcanlar T, Butler L, Windle E, Desouza KC, Mehmood R, Corchado JM (2020) Can building \u201cartificially intelligent cities\u201d safeguard humanity from natural disasters, pandemics, and other catastrophes? an urban scholar\u2019s perspective. Sensors 20(10):2988","journal-title":"Sensors"},{"key":"11891_CR63","doi-asserted-by":"crossref","unstructured":"Yoon K, Gwak J, Song Y, Yoon Y, Jeon M (2020) Oneshotda: Online multi-object tracker with one-shot-learning-based data association, vol 8","DOI":"10.1109\/ACCESS.2020.2975912"},{"issue":"1","key":"11891_CR64","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/s13640-020-0496-6","volume":"2020","author":"Y Yuan","year":"2020","unstructured":"Yuan Y, Chu J, Leng L, Miao J, Kim B (2020) A scale-adaptive object-tracking algorithm with occlusion detection. EURASIP J Image Video Process 2020(1):7","journal-title":"EURASIP J Image Video Process"},{"issue":"19","key":"11891_CR65","doi-asserted-by":"publisher","first-page":"27, 809","DOI":"10.1007\/s11042-019-07898-2","volume":"78","author":"H Zhang","year":"2019","unstructured":"Zhang H, Hong X (2019) Recent progresses on object detection: a brief review. Multimed Tools Appl 78(19):27, 809\u201327, 847","journal-title":"Multimed Tools Appl"},{"key":"11891_CR66","unstructured":"Zhang L, Li Y, Nevatia R (2008) Global data association for multi-object tracking using network flows. In: 2008 IEEE Computer society conference on computer vision and pattern recognition (CVPR 2008). IEEE Computer Society, Anchorage"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11891-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-11891-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11891-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,26]],"date-time":"2024-01-26T10:12:40Z","timestamp":1706263960000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-11891-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,18]]},"references-count":66,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["11891"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-11891-z","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,18]]},"assertion":[{"value":"9 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}