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Context-aware Video Surveillance System
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 Title & Authors
Context-aware Video Surveillance System
An, Tae-Ki; Kim, Moon-Hyun;
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 Abstract
A video analysis system used to detect events in video streams generally has several processes, including object detection, object trajectories analysis, and recognition of the trajectories by comparison with an a priori trained model. However, these processes do not work well in a complex environment that has many occlusions, mirror effects, and/or shadow effects. We propose a new approach to a context-aware video surveillance system to detect predefined contexts in video streams. The proposed system consists of two modules: a feature extractor and a context recognizer. The feature extractor calculates the moving energy that represents the amount of moving objects in a video stream and the stationary energy that represents the amount of still objects in a video stream. We represent situations and events as motion changes and stationary energy in video streams. The context recognizer determines whether predefined contexts are included in video streams using the extracted moving and stationary energies from a feature extractor. To train each context model and recognize predefined contexts in video streams, we propose and use a new ensemble classifier based on the AdaBoost algorithm, DAdaBoost, which is one of the most famous ensemble classifier algorithms. Our proposed approach is expected to be a robust method in more complex environments that have a mirror effect and/or a shadow effect.
 Keywords
Ensemble classifier;Surveillance;Context aware;Video analysis;AdaBoost;
 Language
English
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 References
1.
H.H. Nagel, "From Image Sequences Towards Conceptual Descriptions," in Image and Vision Computing, vol. 6, no. 2, pp. 59-74, May 1988. crossref(new window)

2.
Thomas M. Strat, "Employing Contextual Information in Computer Vision", In Proceedings of ARPA Image Understanding Workshop, pp. 217-229, 1993.

3.
Gerard Medioni, Isaac Cohen, Francois Bremond, Somboon Hongeng and Ramakant Nevatia, "Event Detection and Analysis from Video Streams", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, vo. 8, pp. 873-889, Aug 2001. crossref(new window)

4.
Gary R. Bradski and James W. Davis, "Motion segmentation and pose recognition with motion history gradients", Int. Journal Machine Vision and Applications, vol. 13, vo. 3, pp. 174-184, 2002. crossref(new window)

5.
Aaron Bobick and James Davis, "an appearance-based representation of action", in Proceedings of international conference on Pattern Recognition, vol. 1, pp. 307-312, Vienna, Austria, Aug 1996.

6.
Edward H. Adelson and James R. Bergen, "Spatiotemporal energy models for the perception of motion", Journal Optical Society of America, vol.2, vo.2, pp. 284-299, Feb. 1985. crossref(new window)

7.
Robert E. Schapire and Yoram Singer, "Improved Boosting Algorithms Using Confidence-rated Predictions" Machine Learning, vol. 37, No. 3, pp. 297-336, Dec. 1999. crossref(new window)

8.
Prem Melville and Raymond J. Mooney, "Creating diversity in ensembles using artificial data", Journal of Information Fusion, vol. 6, No. 1, pp. 99-111, Mar. 2004.

9.
Xuchun Li, Lei Wang and Eric Sung, "AdaBoost with SVM-based component classifiers", Engineering Applications of Artificial Intelligence 21, pp. 785-795, 2008. crossref(new window)

10.
Lumila I. Kuncheva and Christopher J. Whitaker, "Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy", Machine Learning, vol. 51, no. 2, pp. 181-207, May 2003. crossref(new window)

11.
Louisa Lam, "Classifier Combinations: Implementations and Theoretical Issues", Multiple Classifier Systems, Lecture Notes in Computer Science, vol. 1857, pp. 78-86, Cagliari, Italy, 2000.

12.
Ron Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection", in Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 1137-1143, 1995.

13.
Thomas G. Dietterich, "An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization", Machine Learning, vol. 40, No. 2, pp. 139-157, Aug. 2000. crossref(new window)

14.
Dianhong Wang and Liangxiao Jiang, "An improved attribute selection measure for decision tree induction", in Proceedings of Fourth International Conference on Fuzzy Systems and Knowledge Discovery, vol. 4, pp. 654-658, 2007.

15.
Paul Viola and Michael Jones, "Robust Real-time Object Detection", International Journal of Computer Vision, vol. 27, no. 2, pp. 137-154, 2004.

16.
Tin Kam Ho, "The Random Subspace Method for Constructing Decision Forests", IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 832-844, Aug 1998. crossref(new window)

17.
Anders Kroggh and Jesper Vedelsby, "Neural Network Ensembles, Cross Validation and Active Learning", Advances in Neural Information Processing Systems, vol. 7, pp. 231-238, 1995.

18.
Ki-Yeol Eom, Tae-Ki AN, Gyu-Jin Kim, Gyu-Jin Jang, Moon-Hyun Kim, "Fast Object Tracking in Intelligent Surveillance System", LNCS 5593, pp. 749-763, July, 2009.

19.
Ki-Yeol Eom, Tae-Ki AN, Gyu-Jin Kim, Gyu-Jin Jang, Jae-Young Jung, Moon-Hyun Kim, "Hierarchically Categorized Performance Evaluation Criteria for Intelligent Surveillance System", in Proceedings of the 2009 International Symposium on Web Information Systems and Applications, pp. 223-226, May. 2009.

20.
Dong-Min Woo, Quoc-Dat Nguyen, "3D Building Detection and Reconstruction from Aerial Images Using Perceptual Organization and Fast Graph Search", Journal of Electrical Engineering & Technology, Vol. 3, No. 3, pp. 436-443, Sep. 2008. crossref(new window)