{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T23:36:33Z","timestamp":1764977793651,"version":"3.46.0"},"reference-count":28,"publisher":"Walter de Gruyter GmbH","issue":"2","license":[{"start":{"date-parts":[[2016,11,17]],"date-time":"2016-11-17T00:00:00Z","timestamp":1479340800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,3,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In the area of modern intelligent systems, the retrieval process of video objects is still a challenging task because objects are usually affected by object confusion, similar appearance among objects, different posing, small size of objects, and interactions among multiple objects. In order to overcome these challenges, the video object is retrieved based on the trajectory points of the multiple-motion objects. However, if an object is in an occlusion situation, the calculation of trajectory points from the objects is considerably altered. In order to overcome the above challenges, we have proposed a technique of query-specific distance and hybrid tracking model for video object retrieval. To verify the performance of the proposed method, five videos were collected from the CAVIAR dataset. Then, the proposed tracking process was applied with these five videos and the performance was analysed based on various parameters, such as precision, recall, and f-measure. From the results, we can prove that the proposed hybrid model attained a higher f-measure of 76.7% compared to that of other existing tracking models, such as the nearest neighbourhood algorithmic model and spatial-exponential weighted moving average model.<\/jats:p>","DOI":"10.1515\/jisys-2016-0106","type":"journal-article","created":{"date-parts":[[2016,11,18]],"date-time":"2016-11-18T08:01:03Z","timestamp":1479456063000},"page":"195-212","source":"Crossref","is-referenced-by-count":5,"title":["Query-Specific Distance and Hybrid Tracking Model for Video Object Retrieval"],"prefix":"10.1515","volume":"27","author":[{"given":"C.A.","family":"Ghuge","sequence":"first","affiliation":[{"name":"K. L. University , Guntur, AP , India"}]},{"given":"Sachin D.","family":"Ruikar","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering , Walchand College of Engineering , Sangli, Maharashtra , India"}]},{"given":"V. Chandra","family":"Prakash","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering , K. L. University , Guntur, AP , India"}]}],"member":"374","published-online":{"date-parts":[[2016,11,17]]},"reference":[{"key":"2025120523303653094_j_jisys-2016-0106_ref_001_w2aab3b7b8b1b6b1ab1b5b1Aa","doi-asserted-by":"crossref","unstructured":"R. Arroyo, J. J. Yebes, L. M. Bergasa, I. G. Daza and J. Almazan, Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls, Expert Syst. Appl. 42 (2015), 7991\u20138005.10.1016\/j.eswa.2015.06.016","DOI":"10.1016\/j.eswa.2015.06.016"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_002_w2aab3b7b8b1b6b1ab1b5b2Aa","doi-asserted-by":"crossref","unstructured":"I. Biederman, Recognition-by-components: a theory of human image understanding, Psychol. Rev.94 (1987), 115\u2013147.10.1037\/0033-295X.94.2.115","DOI":"10.1037\/0033-295X.94.2.115"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_003_w2aab3b7b8b1b6b1ab1b5b3Aa","unstructured":"W. Brendel and S. Todorovic, Video object segmentation by tracking regions, in: Proceedings of IEEE Conference on Computer Vision, vol. 28, pp. 778\u2013785, 2009."},{"key":"2025120523303653094_j_jisys-2016-0106_ref_004_w2aab3b7b8b1b6b1ab1b5b4Aa","doi-asserted-by":"crossref","unstructured":"T. Brox and J. Malik, Large displacement optical flow: descriptor matching invariational motion estimation, IEEE Trans. Pattern Anal. Mach. Intell.33 (2011), 500\u2013513.10.1109\/TPAMI.2010.143","DOI":"10.1109\/TPAMI.2010.143"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_005_w2aab3b7b8b1b6b1ab1b5b5Aa","doi-asserted-by":"crossref","unstructured":"T. Brox and J. Malik, Object segmentation by long term analysis of point trajectories, in: Proceedings of Computer Vision \u2013 ECCV, vol. 6315, pp. 282\u2013295, 2010.","DOI":"10.1007\/978-3-642-15555-0_21"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_006_w2aab3b7b8b1b6b1ab1b5b6Aa","doi-asserted-by":"crossref","unstructured":"Z. Cai, Y. Liang, H. Hu and W. Luo, Offline video object retrieval method based on color features, Comput. Intell. Intell. Syst.575 (2016), 495\u2013505.10.1007\/978-981-10-0356-1_53","DOI":"10.1007\/978-981-10-0356-1_53"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_007_w2aab3b7b8b1b6b1ab1b5b7Aa","doi-asserted-by":"crossref","unstructured":"X. Cao, F. Wang, B. Zhang, H. Fu and C. Li, Unsupervised pixel-level video foreground object segmentation via shortest path algorithm, Neurocomputing 172 (2016), 235\u2013243.10.1016\/j.neucom.2014.12.105","DOI":"10.1016\/j.neucom.2014.12.105"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_008_w2aab3b7b8b1b6b1ab1b5b8Aa","unstructured":"CAVIAR Test Case Scenarios, Available at: http:\/\/homepages.inf.ed.ac.uk\/rbf\/CAVIARDATA1\/, Accessed 25 February, 2016."},{"key":"2025120523303653094_j_jisys-2016-0106_ref_009_w2aab3b7b8b1b6b1ab1b5b9Aa","doi-asserted-by":"crossref","unstructured":"H. Y. Cheng and J. N. Hwang, Integrated video object tracking with applications in trajectory-based event detection, Vis. Commun. Image Represent.22 (2011), 673\u2013685.10.1016\/j.jvcir.2011.07.001","DOI":"10.1016\/j.jvcir.2011.07.001"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_010_w2aab3b7b8b1b6b1ab1b5c10Aa","doi-asserted-by":"crossref","unstructured":"F. Chevalier, M. Delest and J. Domenger, A heuristic for the retrieval of objects in low resolution video, in: Proceedings of International Workshop on Content-Based Multimedia Indexing, pp. 144\u2013151, 2007.","DOI":"10.1109\/CBMI.2007.385404"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_011_w2aab3b7b8b1b6b1ab1b5c11Aa","doi-asserted-by":"crossref","unstructured":"C. H. Chuang, S. C. Cheng, C. C. Chang and P. P. Chen, Model-based approach to spatial-temporal sampling of video clips for video object detection by classification, Vis. Commun. Image Represent.25 (2014), 1018\u20131030.10.1016\/j.jvcir.2014.02.014","DOI":"10.1016\/j.jvcir.2014.02.014"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_012_w2aab3b7b8b1b6b1ab1b5c12Aa","doi-asserted-by":"crossref","unstructured":"H. Diez-Rodriguez, G. Morales-Luna and J. O. Olmedo-Aguirre, Ontology-based knowledge retrieval, in: Proceedings of International Conference on Artificial Intelligence, pp. 23\u201328, 2008.","DOI":"10.1109\/MICAI.2008.25"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_013_w2aab3b7b8b1b6b1ab1b5c13Aa","doi-asserted-by":"crossref","unstructured":"B. Erol and F. Kossentini, Shape-based retrieval of video objects, IEEE Trans. Multimed.7 (2005), 179.10.1109\/TMM.2004.840607","DOI":"10.1109\/TMM.2004.840607"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_014_w2aab3b7b8b1b6b1ab1b5c14Aa","doi-asserted-by":"crossref","unstructured":"I. G\u00f3mez-Conde and D. N. Olivieri, A KPCA spatio-temporal differential geometric trajectory cloud classifier for recognizing human actions in a CBVR system, Expert Syst. Appl.42 (2015), 5472\u20135490.10.1016\/j.eswa.2015.03.010","DOI":"10.1016\/j.eswa.2015.03.010"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_015_w2aab3b7b8b1b6b1ab1b5c15Aa","doi-asserted-by":"crossref","unstructured":"J. G\u00f3mez-Romero, M. A. Patricio, J. Garc\u00eda and J. M. Molina, Ontology-based context representation and reasoning for object tracking and scene interpretation in video, Exp. Syst. Appl.38 (2011), 7494\u20137510.10.1016\/j.eswa.2010.12.118","DOI":"10.1016\/j.eswa.2010.12.118"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_016_w2aab3b7b8b1b6b1ab1b5c16Aa","doi-asserted-by":"crossref","unstructured":"J. Gong and C. H. Caldas, An object recognition, tracking, and contextual reasoning-based video interpretation method for rapid productivity analysis of construction operations, Autom. Construct.20 (2011), 1211\u20131226.10.1016\/j.autcon.2011.05.005","DOI":"10.1016\/j.autcon.2011.05.005"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_017_w2aab3b7b8b1b6b1ab1b5c17Aa","doi-asserted-by":"crossref","unstructured":"R. Hu and J. Collomosse, Motion-sketch based video retrieval using a Trellis Levenshtein distance, in: Proceedings of International Conference on Pattern Recognition, pp. 121\u2013124, 2010.","DOI":"10.1109\/ICPR.2010.38"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_018_w2aab3b7b8b1b6b1ab1b5c18Aa","doi-asserted-by":"crossref","unstructured":"Y. Jing, M. Covell, D. Tsai and J. M. Rehg, Learning query-specific distance functions for large-scale web image search, IEEE Trans. Multimed.15 (2013), 2022\u20132034.10.1109\/TMM.2013.2279663","DOI":"10.1109\/TMM.2013.2279663"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_019_w2aab3b7b8b1b6b1ab1b5c19Aa","doi-asserted-by":"crossref","unstructured":"L. Kratz and K. Nishino, Tracking pedestrians using local spatio-temporal motion patterns in extremely crowded scenes, IEEE Trans. Pattern Anal. Mach. Intell.34 (2012), 987\u20131002.10.1109\/TPAMI.2011.173","DOI":"10.1109\/TPAMI.2011.173"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_020_w2aab3b7b8b1b6b1ab1b5c20Aa","doi-asserted-by":"crossref","unstructured":"Y. H. Lai and C. K. Yang, Video object retrieval by trajectory and appearance, IEEE Trans. Circ. Syst. Video Technol.25 (2015), 1026\u2013103.10.1109\/TCSVT.2014.2358022","DOI":"10.1109\/TCSVT.2014.2358022"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_021_w2aab3b7b8b1b6b1ab1b5c21Aa","unstructured":"W. N. Lie and W. C. Hsiao, Content-based video retrieval based on object motion trajectory, in: Proceedings of Multimedia Signal Processing, pp. 237\u2013240, 2002."},{"key":"2025120523303653094_j_jisys-2016-0106_ref_022_w2aab3b7b8b1b6b1ab1b5c22Aa","doi-asserted-by":"crossref","unstructured":"K. Liu, B. Liu, E. Blasch, D. Shen, Z. Wang, H. Ling and G. Chen, A cloud infrastructure for target detection and tracking using audio and video fusion, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2015.","DOI":"10.1109\/CVPRW.2015.7301299"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_023_w2aab3b7b8b1b6b1ab1b5c23Aa","doi-asserted-by":"crossref","unstructured":"A. H. Mazinan, A. Amir-Latifi and M. F. Kazemi, A knowledge-based objects tracking algorithm in color video using Kalman filter approach, in: Proceedings of International Conference on Information Retrieval & Knowledge Management, pp. 50\u201353, 2012.","DOI":"10.1109\/InfRKM.2012.6205034"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_024_w2aab3b7b8b1b6b1ab1b5c24Aa","doi-asserted-by":"crossref","unstructured":"M. Safar, C. Shahabi and X. Sun, Image retrieval by shape: a comparative study, in: Proceedings of IEEE International conference on Multimedia and Exposition, vol. 1, pp. 141\u2013144, 2000.","DOI":"10.1109\/ICME.2000.869564"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_025_w2aab3b7b8b1b6b1ab1b5c25Aa","doi-asserted-by":"crossref","unstructured":"J. Sivic and A. Zisserman, Efficient visual search of videos cast as text retrieval, IEEE Trans. Pattern Anal.31 (2009), 591\u2013606.10.1109\/TPAMI.2008.111","DOI":"10.1109\/TPAMI.2008.111"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_026_w2aab3b7b8b1b6b1ab1b5c26Aa","unstructured":"J. Sivic, F. Schaffalitzky and A. Zisserman, Efficient object retrieval from videos, in: Proceedings of 12th European Conference on Signal Processing, pp. 1737\u20131740, 2004."},{"key":"2025120523303653094_j_jisys-2016-0106_ref_027_w2aab3b7b8b1b6b1ab1b5c27Aa","doi-asserted-by":"crossref","unstructured":"L. F. Teixeira and L. Corte-Real, Video object matching across multiple independent views using local descriptors and adaptive learning, Pattern Recognit.30 (2009), 157\u2013167.10.1016\/j.patrec.2008.04.001","DOI":"10.1016\/j.patrec.2008.04.001"},{"key":"2025120523303653094_j_jisys-2016-0106_ref_028_w2aab3b7b8b1b6b1ab1b5c28Aa","doi-asserted-by":"crossref","unstructured":"P. Turaga and R. Chellappa, Nearest-neighbor search algorithms on non-Euclidean manifolds for computer vision applications, in Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing, pp. 282\u2013289, 2010.","DOI":"10.1145\/1924559.1924597"}],"container-title":["Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.degruyter.com\/view\/j\/jisys.2018.27.issue-2\/jisys-2016-0106\/jisys-2016-0106.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2016-0106\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2016-0106\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T23:32:21Z","timestamp":1764977541000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2016-0106\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,17]]},"references-count":28,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2017,1,20]]},"published-print":{"date-parts":[[2018,3,28]]}},"alternative-id":["10.1515\/jisys-2016-0106"],"URL":"https:\/\/doi.org\/10.1515\/jisys-2016-0106","relation":{},"ISSN":["2191-026X","0334-1860"],"issn-type":[{"type":"electronic","value":"2191-026X"},{"type":"print","value":"0334-1860"}],"subject":[],"published":{"date-parts":[[2016,11,17]]}}}