Abstract
This paper presents an approach for object detection and event recognition in video surveillance scenarios. The proposed system utilizes a Histogram of Oriented Gradients (HOG) method for object detection, and a Hidden Markov Model (HMM) for capturing the temporal structure of the features. Decision making is based on the understanding of objects motion trajectory and the relationships between objects’ movement and events. The proposed method is applied to recognize events from the public PETS and i-LIDS datasets, which include vehicle events such as U-turns and illegal parking, as well as abandoned luggage recognition established by set of rules. The effectiveness of the proposed solution is demonstrated through extensive experimentation.
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© 2011 Springer-Verlag Berlin Heidelberg
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Wang, Ch., Wang, Y., Guan, L. (2011). Event Detection and Recognition using Histogram of Oriented Gradients and Hidden Markov Models. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21593-3_44
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DOI: https://doi.org/10.1007/978-3-642-21593-3_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21592-6
Online ISBN: 978-3-642-21593-3
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