Abstract
Recently, abnormal event detection has attracted great research attention because of its wide range of applications. In this paper, we propose an hybrid method combining both tracking output and motion templates. This method consists of two steps: object detection, localization and tracking and abnormal event detection. Our contributions in this paper are three-folds. Firstly, we propose a method that apply only HOG-SVM detector on extended regions detected by background subtraction. This method takes advantages of the background subtraction method (fast computation) and the HOG-SVM detector (reliable detection). Secondly, we do multiple objects tracking based on HOG descriptor. The HOG descriptor, computed in the detection phase, will be used in the phase of observation and track association. This descriptor is more robust than usual grayscale (color) histogram based descriptor. Finally, we propose a hybrid method for abnormal event detection this allows to remove several false detection cases.
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Acknowledgments
The research leading to this paper was supported by the National Project B2013.01.41 “Study and develop an abnormal event recognition system based on computer vision techniques”. We would like to thank the project and people involved in this project.
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Le, TL., Tran, TH. (2015). Real-Time Abnormal Events Detection Combining Motion Templates and Object Localization. In: Dang, Q., Nguyen, X., Le, H., Nguyen, V., Bao, V. (eds) Some Current Advanced Researches on Information and Computer Science in Vietnam. NAFOSTED 2014. Advances in Intelligent Systems and Computing, vol 341. Springer, Cham. https://doi.org/10.1007/978-3-319-14633-1_2
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DOI: https://doi.org/10.1007/978-3-319-14633-1_2
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