Abstract
Background subtraction method based on mixture of Gaussians was employed to detect all regions in a video frame denoting moving objects. Kalman filters were used for establishing relations between the regions and real moving objects in a scene and for tracking them continuously. The objects were represented by rectangles. The objects coupling with adequate regions including the relation of many-to-many was studied experimentally employing Kalman filters. The implemented algorithm provides a part of an advanced audio-video surveillance system for security applications which is described briefly in the paper.
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References
Czyzewski, A., Dalka, P.: Visual Traffic Noise Monitoring in Urban Areas. International Journal of Multimedia and Ubiquitous Engineering 2(2), 91–101 (2007)
Li, H., Ngan, K.: Automatic Video Segmentation and Tracking for Content-Based Applications. IEEE Communication Magazine 45(1), 27–33 (2007)
Liu, Y., Zheng, Y.: Video Object Segmentation and Tracking Using y-Learning Classification. IEEE Trans. Circuits and Syst. For Video Tech. 15(7), 885–899 (2005)
Konrad, J.: Videopsy: Dissecting Visual Data in Space Time. IEEE Communication Magazine 45(1), 34–42 (2007)
Yang, T., Li, S., Pan, Q., Li, J.: Real-Time and Accurate Segmentation of Moving Objects in Dynamic Scene. In: ACM Multimedia 2nd International Workshop on Video Surveillance and Sensor Networks, New York, October 10-16 (2004)
Stauffer, C., Grimson, W.: Learning patterns of activity using real-time tracking. IEEE Trans. on Pattern Analysis and Machine Intell. 22(8), 747–757 (2000)
Elgammal, A., Harwood, D., Davis, L.: Non Parametric Model for Background Subtraction. In: ICCV Frame-rate Workshop (September 1999)
Dalka, P.: Detection and Segmentation of Moving Vehicles and Trains Using Gaussian Mixtures, Shadow Detection and Morphological Processing. Machine Graphics and Vision 15(3/4), 339–348 (2006)
Welch, G., Bishop, G.: An Introduction To the Kalman Filter. Technical Report TR95-041, University of North Carolina at Chapel Hill (1995)
Funk, N.: A Study of the Kalman Filter applied to Visual Tracking. University of Alberta, Project for CMPUT 652 (December 7, 2003)
Yu, H., Wang, Y., Kuang, F., Wan, Q.: Multi-moving Targets Detecting and Tracking in a Surveillance System. In: Proc. of the 5th World Congress on Intelligent Control and Automation, Hangzhou, China, June 15-19 (2004)
MartÃnez-del-Rincón, J., Herrero-Jaraba, J.E., Gómez, J.R., Orrite-Uruñuela, C.: Automatic left luggage detection and tracking using multi-camera UKF. In: Proc. 9th IEEE Internat. Workshop on Performance Evaluation in Tracking and Surveillance (PETS 2006), NY, USA, pp. 59–66 (2006)
Lv, F., Song, X., Wu, B., Kumar, V., Nevatia, S.: Left-Luggage Detection using Bayesian Inference. In: Proc. of 9th IEEE Int. Wrokshop on Performance Evaluation of Tracking and Surveillance, New York, USA, June 2006, pp. 83–90 (2006)
PETS 2006 – a collection of test recordings from the Ninth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, New York, USA, June 18 (2006)
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Czyzewski, A., Dalka, P. (2008). Moving Object Detection and Tracking for the Purpose of Multimodal Surveillance System in Urban Areas. In: Tsihrintzis, G.A., Virvou, M., Howlett, R.J., Jain, L.C. (eds) New Directions in Intelligent Interactive Multimedia. Studies in Computational Intelligence, vol 142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68127-4_8
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DOI: https://doi.org/10.1007/978-3-540-68127-4_8
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