Abstract
Multi-target tracking is a active research field nowadays due to its wide practical applicability in video processing. While talking about Multi-target tracking, ‘multi-target occlusion’ is a common problem that needs to be addressed. Lots of work has been done using multiple cameras for handling ‘multitarget occlusion’; however most of them require camera calibration parameters that make them impractical for outdoor video surveillance applications. The main focus of this paper is to reduce the dependency on camera calibration for multiple camera collaboration. In this perspective Gale-Shapley algorithm (GSA) has been used for finding stable matching between two or more camera views, while more robustness on tracking of objects has been ensured by combining multiple cues such object’s boundary information of the object with color histogram. Efficient tracking of object ensures proficient reckoning of target depicting parameter (i.e. apparent color, height and width information of the object) as a consequence better camera collaboration. The simulation results prove the validity of our approach.
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References
Qu, W., Schonfeld, D., Mohamed, M.: Distributed Bayesian multiple-target tracking in crowded environments using multiple collaborative cameras. EURASIP Journal on Applied Signal Processing (1), 21–21 (2007)
Bar-Shalom, Y., Jammer, A.G.: Tracking and Data Association. Academic Press, San Diego (1998)
Hue, C., Cadre, J.-P.L., P´erez, P.: Sequential Monte Carlo methods for multiple target tracking and data fusion. IEEE Transactions on Signal Processing 50(2), 309–325 (2002)
MacCormick, J., Blake, A.: A probabilistic exclusion principle for tracking multiple objects. International Journal of Computer Vision 39(1), 57–71 (2000)
Gordon, N.: A hybrid bootstrap filter for target tracking in clutter. IEEE Transactions on Aerospace and Electronic Systems 33(1), 353–358 (1997)
Zhao, T., Nevatia, R.: Tracking multiple humans in crowded environment. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2004), Washington, DC, USA, vol. 2, pp. 406–413 (June-July 2004)
Zhao, T., Nevatia, R.: Tracking multiple humans in complex situations. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9), 1208–1221 (2004)
Tao, H., Sawhney, H., Kumar, R.: A sampling algorithm for detection and tracking multiple objects. In: Proceedings of IEEE International Conference on Computer Vision (ICCV 1999) Workshop on Vision Algorithm, Corfu, Greece (September 1999)
Khan, Z., Balch, T., Dellaert, F.: An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 279–290. Springer, Heidelberg (2004)
Smith, K., Gatica-Perez, D., Odobez, J.-M.: Using particles to track varying numbers of interacting people. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, Calif, USA, vol. 1, pp. 962–969 (June 2005)
McKenna, S.J., Jabri, S., Duric, Z., Rosenfeld, A., Wechsler, H.: Tracking groups of people. Computer Vision and Image Understanding 80(1), 42–56 (2000)
Lee, L., Romano, R., Stein, G.: Monitoring activities from multiple video streams: Establishing a common coordinate frame. IEEE Transactions on Pattern Analysis and Machine Intelligence, 758–767 (2000); Special Issue on Video Surveillance and Monitoring
Chang, T.-h., Gong, S.: Tracking Multiple People with a Multi-Camera System. In: IEEE Workshop on Multi-Object Tracking (2001)
Yu, T., Wu, Y.: Collaborative tracking of multiple targets. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2004), Washington, DC, USA, vol. 1, pp. 834–841 (June-July 2004)
Wu, Y., Hua, G., Yu, T.: Tracking articulated body by dynamic Markov network. In: Proceedings of 9th IEEE International Conference on Computer Vision (ICCV 2003), Nice, France, vol. 2, pp. 1094–1101 (October 2003)
Qu, W., Schonfeld, D., Mohamed, M.: Real-time interactively distributed multi-object tracking using a magnetic-inertia potential model. In: Proceedings of 10th IEEE International Conference on Computer Vision (ICCV 2005), Beijing, China, vol. 1, pp. 535–540 (October 2005)
Cai, Q., Aggarwal, J.K.: Tracking human motion in structured environments using a distributed-camera system. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(11), 1241–1247 (1999)
Kelly, P.H., Katkere, A., Kuramura, D.Y., Moezzi, S., Chatterjee, S., Jain, R.: An architecture for multiple perspective interactive video. In: Proceedings of 3rd ACM International Conference on Multimedia (ACM Multimedia 1995), San Francisco, Calif, USA, pp. 201–212 (November 1995)
Black, J., Ellis, T.: Multiple camera image tracking. In: Proceedings of 2nd IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS 2001), Kauai,Hawaii, USA (December 2001)
Lee, L., Romano, R., Stein, G.: Monitoring activities from multiple video streams: establishing a common coordinate frame. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 758–767 (2000)
Hue, C., Le Cadre, J.-P., Pérez, P.: Sequential monte carlo methods for multiple target tracking and data fusion. IEEE Transactions on Signal Processing 50(2), 309–325 (2002)
Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)
Gale, D., Shapley, L.S.: College admissions and the stability of marriage. American Mathematical Monthly 69, 9–14 (1962)
Guraya, F.F.E., Bayle, P.-Y., Cheikh, F.A.: People tracking via a modified CAMSHIFT algorithm (2009)
Maskell, S., Gordon, N.: A tutorial on particle Filters for on-line nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing 50, 174–188 (2001)
Kitagawa, G.: Monte Carlo: Filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics 5(1), 1–25 (1996)
Chen, T., Lin, Y.-C., Fang, W.-H.: A Video-Based Human Fall Detection System For Smart Homes. YieJournal of the Chinese Institute of Engineers 33(5), 681–690 (2010)
Nummiaro, K., Koller-Meier, E., Gool, L.V., Gaal, L.V.: Object tracking with an adaptive color-based particle Filter (2002), http://www.koller-meier.ch/esther/dagm2002.pdf
30. Bouguet, J. Y.: Pyramidal implementation of the Lucas Kanade feature tracker: Description of the algorithm. Intel Corporation Microprocessor Research Labs (2002), http://robots.stanford.edu/cs223b04/algo_tracking.pdf
Blake, A., Isard, M.: The Condensation algorithm - conditional density propagation and applications to visual tracking. In: Advances in Neural Information Processing Systems (NIPS 1996), December 2-5, pp. 36–41. The MIT Press, Denver (1996)
Zhao, S., Zhao, J., Wang, Y., Fu, X.: Moving object detecting using gradient information, three-frame-differencing and connectivity testing. In: Sattar, A., Kang, B.-h. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 510–518. Springer, Heidelberg (2006)
Rudakova, V.: Probabilistic framework for multi-target tracking using multi-camera: applied to fall detection. Master thesis, Gjøvik University College (2010)
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Rudakova, V., Saha, S.K., Alaya Cheikh, F. (2011). Multiple Collaborative Cameras for Multi-Target Tracking Using Color-Based Particle Filter and Contour Information. In: Cherifi, H., Zain, J.M., El-Qawasmeh, E. (eds) Digital Information and Communication Technology and Its Applications. DICTAP 2011. Communications in Computer and Information Science, vol 166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21984-9_27
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DOI: https://doi.org/10.1007/978-3-642-21984-9_27
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