Abstract
Tracking objects in video is a very challenging research topic, particularly when people in groups are tracked, with partial and full occlusions and group dynamics being common difficulties. Hence, its necessary to deal with group tracking, formation and separation, while assuring the overall consistency of the individuals. This paper proposes enhancements to a group management and tracking algorithm that receives information of the persons in the scene, detects the existing groups and keeps track of the persons that belong to it. Since input information for group management algorithms is typically provided by a tracking algorithm and it is affected by noise, mechanisms for handling such noisy input tracking information were also successfully included. Performed experiments demonstrated that the described algorithm outperformed state-of-the-art approaches.
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References
Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 743–761 (2012)
Nguyen, D.T., Li, W., Ogunbona, P.O.: Human detection from images and videos: a survey. Pattern Recogn. 51, 148–175 (2016)
Aggarwal, J.R., Ryoo, M.S.: Human activity analysis: a review. ACM Comput. Surv. 43, 16:1–16:43 (2011)
Smeulders, A.W., Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: Visual tracking: an experimental survey. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1442–1468 (2014)
Gauquelin, M., Gauquelin, F.: Dicionário de Psicologia: as idéias, as obras, os homens. Centre d’Étude et de Promotion de la Lecture, Paris (1987)
Junior, S.J., et al.: Crowd analysis using computer vision techniques. IEEE Signal Process. Mag. 27(5), 66–77 (2010)
Kong, D., Gray, D., Tao, H.: A viewpoint invariant approach for crowd counting in Pattern Recognition. In: 18th International Conference on ICPR 2006, vol. 3, pp. 1187–1190. IEEE (2006)
Ali, S., Shah, M.: A lagrangian particle dynamics approach for crowdow segmentation and stability analysis. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–6. IEEE (2007)
Kilambi, P., Ribnick, E., Joshi, A.J., Masoud, O., Papanikolopoulos, N.: Estimating pedestrian counts in groups. Comput. Vis. Image Underst. 110(1), 43–59 (2008)
Bazzani, L., Cristani, M., Murino, V.: Decentralized particle filter for joint individual-group tracking. In: IEEE Conference on. Computer Vision and Pattern Recognition (CVPR), pp. 1886–1893. IEEE (2012)
Chen, T., Schon, T.B., Ohlsson, H., Ljung, L.: Decentralized particle filter with arbitrary state decomposition. IEEE Trans. Signal Process. 59(2), 465–478 (2011)
Gárate, C., Zaidenberg, S., Badie, J., Brémond, F., et al.: Group tracking and behavior recognition in long video surveillance sequences. In: International Conference on Computer Vision Theory and Applications (VISAPP), vol. 2. IEEE (2014)
Bak, S., Corvee, E., Bremond, F., Thonnat, M.: Multiple-shot human re-identification by mean Riemannian covariance grid. In: 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), pp. 179–184. IEEE (2011)
Caviar dataset. http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1/. (Accessed on 08 February 2015)
Biwi dataset. http://www.vision.ee.ethz.ch/datasets/index.en.html. (Accessed on 09 February 2015)
Friends meet dataset. http://www.iit.it/en/datasets-and-code/datasets/fmdataset.html. (Accessed on 09 February 2015)
Yin, F., Makris, D., Velastin, S.A.: Performance evaluation of object tracking algorithms. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, Rio De Janeiro (2007)
Acknowledgment
This work was partially funded by Project “TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020", financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).
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Pereira, A., Familiar, A., Moreira, B., Terroso, T., Carvalho, P., Côrte-Real, L. (2016). Video Based Group Tracking and Management. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_48
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DOI: https://doi.org/10.1007/978-3-319-41501-7_48
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