Abstract
Object classification in videos is an important step in many applications such as abnormal event detection in video surveillance, traffic analysis is urban scenes and behavior control in crowded locations. In this work, propose a framework for moving object classification in farfield videos. Much works have been dedicated to accomplish this task. We overview existing works and combine several techniques to implement a real time object classifier with offline training phase. We follow three main steps to classify objects in steady background videos : background subtraction, object tracking and classification. We measure accuracy of our classifier by experiments done using the PETS 2009 dataset.
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References
Gouet-Brunet, V., Lameyre, B.: Object recognition and segmentation in videos by connecting heterogeneous visual features. Comput. Vis. Image Underst. 111(1), 86–109 (2008)
Gurwicz, Y., Yehezkel, R., Lachover, B.: Multiclass object classification for real-time video surveillance systems. Pattern Recognit. Lett. 32(6), 805–815 (2011)
Chen, L., Feris, R., Zhai, Y., Brown, L., Hampapur, A.: An integrated system for moving object classification in surveillance videos. In: IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, AVSS 2008, pp. 52–59, Sept 2008
Moeslund, T.B., Hilton, A., Krger, V.: A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Underst. 104(23), 90–126 (2006). Special Issue on Modeling People: vision-based understanding of a persons shape, appearance, movement and behaviour
Hu, W., Tan, T., Wang, L., Mayban, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 34(3), 334–352 (2004)
Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: Content-based multimedia information retrieval: state of the art and challenges. ACM Trans. Multimed. Comput. Commun. Appl. 2(1), 1–19 (2006)
Bose, B., Grimson, E.: Improving object classification in far-field video. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 2, pp. II-673–II-680, June 2004
Setitra, I., Larabi, S.: Background subtraction algorithms with post processing a review. In: 2014 22nd International Conference on Pattern Recognition (ICPR), Aug 2014
Kalman, R.E.: A new approach to linear filtering and prediction problems. ASME J. Basic Eng. 82, 35–45 (1960)
Jazwinski, A.H.: Stochastic Processes and Filtering Theory. Mathematics in science and engineering, vol. 64. Academic Press, New York (1970)
Arulampalam, M.S., Maskell, S., Gordon, N.: A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Trans. Signal Process. 50, 174–188 (2002)
Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach. Prentice Hall Professional Technical Reference, Upper Saddle River (2002)
Lowe, D.G.: Robust model-based motion tracking through the integration of search and estimation. Int. J. Comput. Vision 8(2), 113–122 (1992)
Drummond, T., Cipolla, R.: Real-time tracking of complex structures with on-line camera calibration. Image Vis. Comput. 20(56), 427–433 (2002)
Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 142–149 (2000)
Jepson, A.D., Fleet, D.J., El-Maraghi, T.F.: Robust online appearance models for visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(10), 1296–1311 (2003)
Hota, R.N., Venkoparao, V., Rajagopal, A.: Shape based object classification for automated video surveillance with feature selection. In: Proceedings of the 10th International Conference on Information Technology, ICIT 2007, pp. 97–99. IEEE Computer Society, Washington (2007)
Deselaers, T., Heigold, G., Ney, H.: Object classification by fusing svms and gaussian mixtures. Pattern Recogn. 43(7), 2476–2484 (2010)
Han, S., Vasconcelos, N.: Complex discriminant features for object classification. In: 15th IEEE International Conference on Image Processing, ICIP 2008, pp. 1700–1703, Oct 2008
Song, Z., Chen, Q., Huang, Z., Hua, Y., Yan, S.: Contextualizing object detection and classification. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1585–1592, June 2011
Zhang, Z., Li, M., Huang, K., Tan, T.: Boosting local feature descriptors for automatic objects classification in traffic scene surveillance. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4, Dec 2008
Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 747–757 (2000)
Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly Media, Sebastopol (2008)
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© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Setitra, I., Larabi, S., Uno, T. (2015). A Framework for Object Classification in Farfield Videos. In: Mumtaz, S., Rodriguez, J., Katz, M., Wang, C., Nascimento, A. (eds) Wireless Internet. WICON 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 146. Springer, Cham. https://doi.org/10.1007/978-3-319-18802-7_23
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DOI: https://doi.org/10.1007/978-3-319-18802-7_23
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