Abstract
Video object detection and tracking in surveillance scenarios is a difficult task due to several challenges caused by environmental variations, scene dynamics and noise introduced by the CCTV camera itself. In this paper, we analyse the performance of an object detector and tracker based on background subtraction followed by a graph matching procedure for data association. The analysis is performed based on the CLEAR dataset. In particular, we discuss a set of solutions to improve the robustness of the detector in case of various types of natural light changes, sensor noise, missed detection and merged objects. The proposed solutions and various parameter settings are analysed and compared based on 1 hour 21 minutes of CCTV surveillance footage and its associated ground truth and the CLEAR evaluation metrics.
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References
Stauffer, C., Grimson, W.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Machine Intell. 22, 747–757 (2000)
Taj, M., Maggio, E., Cavallaro, A.: Multi-feature graph-based object tracking. In: Stiefelhagen, R., Garofolo, J.S. (eds.) CLEAR 2006. LNCS, vol. 4122, pp. 190–199. Springer, Heidelberg (2007)
Cavallaro, A., Ebrahimi, T.: Interaction between high-level and low-level image analysis for semantic video object extraction. EURASIP Journal on Applied Signal Processing 6, 786–797 (2004)
Wu, B., X., Kuman, V., Nevatia, R.: Evaluation of USC Human Tracking System for Surveillance Videos. In: Stiefelhagen, R., Garofolo, J.S. (eds.) CLEAR 2006. LNCS, vol. 4122, pp. 183–189. Springer, Heidelberg (2007)
Pnevmatikakis, A., Polymenakos, L., Mylonakis, V.: The ait outdoors tracking system for pedestrians and vehicles. In: Stiefelhagen, R., Garofolo, J.S. (eds.) CLEAR 2006. LNCS, vol. 4122, pp. 171–182. Springer, Heidelberg (2007)
Zhai, Y., Berkowitz, P., Miller, A., Shafique, K., Vartak, A., White, B., Shah, M.: Multiple vehicle tracking in surveillance video. In: Stiefelhagen, R., Garofolo, J.S. (eds.) CLEAR 2006. LNCS, vol. 4122, pp. 200–208. Springer, Heidelberg (2007)
Abd-Almageed, W., Davis, L.: Robust appearance modeling for pedestrian and vehicle tracking. In: Stiefelhagen, R., Garofolo, J.S. (eds.) CLEAR 2006. LNCS, vol. 4122, pp. 209–215. Springer, Heidelberg (2007)
Song, X., Nevatia, R.: Robust vehicle blob tracking with split/merge handling. In: Stiefelhagen, R., Garofolo, J.S. (eds.) CLEAR 2006. LNCS, vol. 4122, pp. 216–222. Springer, Heidelberg (2007)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, Kauai, Hawaii, pp. 511–518 (2001)
Wu, B., Nevatia, R.: Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors. In: Proc. of IEEE Int. Conf. on Computer Vision, pp. 90–97. IEEE Computer Society Press, Washington (2005)
Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: Proc. of Int. Conf. on Computer Vision Systems, vol. 2, pp. 734–741 (2003)
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Technical report, Department of Statistics, Stanford University (1998)
Munder, S., Gavrila, D.: An experimental study on pedestrian classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(11), 1863–1868 (2006)
Herman, S.: A Particle Filtering Approach to Joint Passive Radar Tracking and Target Classification. PhD thesis, University of Illinois at Urbana Champaign (2005)
Shafique, K., Shah, M.: A noniterative greedy algorithm for multiframe point correspondence. IEEE Trans. Pattern Anal. Machine Intell. 27, 51–65 (2005)
Maggio, E., Piccardo, E., Regazzoni, C., Cavallaro, A.: Particle phd filter for multi-target visual tracking. In: Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, Honolulu, USA (2007)
Kasturi, R.: Performance evaluation protocol for face, person and vehicle detection & tracking in video analysis and content extraction (VACE-II). Computer Science & Engineering University of South Florida, Tampa (2006)
Li, W., Unbehauen, J.L.R.: Wavelet based nonlinear image enhancement for gaussian and uniform noise. In: Proc. of IEEE Int. Conf. on Image Processing, Chicago, Illinois, USA, vol. 1, pp. 550–554 (1998)
Aiazzi, B., Baronti, S., Alparone, L.: Multiresolution adaptive filtering of signal-dependent noise based on a generalized laplacian pyramid. In: Proc. of IEEE Int. Conf. on Image Processing, Washington, DC, USA, vol. 1, pp. 381–384 (1997)
Hu, W., Hu, M., Zhou, X., Lou, J.: Principal axis-based correspondence between multiple cameras for people tracking. IEEE Trans. Pattern Anal. Machine Intell. 28(4), 663 (2006)
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Taj, M., Maggio, E., Cavallaro, A. (2008). Objective Evaluation of Pedestrian and Vehicle Tracking on the CLEAR Surveillance Dataset. In: Stiefelhagen, R., Bowers, R., Fiscus, J. (eds) Multimodal Technologies for Perception of Humans. RT CLEAR 2007 2007. Lecture Notes in Computer Science, vol 4625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68585-2_13
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DOI: https://doi.org/10.1007/978-3-540-68585-2_13
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