A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A Real-Time System for Classification of Moving Objects
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
A short introduction to learning with kernels
Advanced lectures on machine learning
Boosting Object Detection Using Feature Selection
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Collaborative signal processing for target tracking in distributed wireless sensor networks
Journal of Parallel and Distributed Computing
Collaborative Target Classification for Image Recognition in Wireless Sensor Networks
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Distributed visual-target-surveillance system in wireless sensor networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper describes a method of categorizing the moving objects using eigen-features and support vector machines. Eigen-features, generally used in face recognition and static image classification, are applied to classify the moving objects detected from the surveillance video sequences. Through experiments on a large set of data, it has been found out that in such an application the binary image instead of the normally used grey image is the more suitable format for the feature extraction. Different SVM kernels have been compared and the RBF kernel is selected as the optimal one. A voting mechanism is employed to utilize the tracking information to further improve the classification accuracy. The resulting labeled object trajectories provide important hints for understanding human activities in the surveillance video.