Abstract
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.
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Lu, S., Zhang, J., Feng, D. (2005). Classification of Moving Humans Using Eigen-Features and Support Vector Machines. In: Gagalowicz, A., Philips, W. (eds) Computer Analysis of Images and Patterns. CAIP 2005. Lecture Notes in Computer Science, vol 3691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11556121_64
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DOI: https://doi.org/10.1007/11556121_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28969-2
Online ISBN: 978-3-540-32011-1
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