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Action Detection by Fusing Hierarchically Filtered Motion with Spatiotemporal Interest Point Features

Action Detection by Fusing Hierarchically Filtered Motion with Spatiotemporal Interest Point Features

YingLi Tian, Liangliang Cao, Zicheng Liu, Zhengyou Zhang
Copyright: © 2013 |Pages: 19
ISBN13: 9781466636828|ISBN10: 1466636823|EISBN13: 9781466636835
DOI: 10.4018/978-1-4666-3682-8.ch012
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MLA

Tian, YingLi, et al. "Action Detection by Fusing Hierarchically Filtered Motion with Spatiotemporal Interest Point Features." Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security, edited by Hans W. Guesgen and Stephen Marsland, IGI Global, 2013, pp. 249-267. https://doi.org/10.4018/978-1-4666-3682-8.ch012

APA

Tian, Y., Cao, L., Liu, Z., & Zhang, Z. (2013). Action Detection by Fusing Hierarchically Filtered Motion with Spatiotemporal Interest Point Features. In H. Guesgen & S. Marsland (Eds.), Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security (pp. 249-267). IGI Global. https://doi.org/10.4018/978-1-4666-3682-8.ch012

Chicago

Tian, YingLi, et al. "Action Detection by Fusing Hierarchically Filtered Motion with Spatiotemporal Interest Point Features." In Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security, edited by Hans W. Guesgen and Stephen Marsland, 249-267. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3682-8.ch012

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Abstract

This chapter addresses the problem of action detection from cluttered videos. In recent years, many feature extraction schemes have been designed to describe various aspects of actions. However, due to the difficulty of action detection, e.g., the cluttered background and potential occlusions, a single type of feature cannot effectively solve the action detection problems in cluttered videos. In this chapter, the authors propose a new type of feature, Hierarchically Filtered Motion (HFM), and further investigate the fusion of HFM with Spatiotemporal Interest Point (STIP) features for action detection from cluttered videos. In order to effectively and efficiently detect actions, they propose a new approach that combines Gaussian Mixture Models (GMMs) with Branch-and-Bound search to locate interested actions in cluttered videos. The proposed new HFM features and action detection method have been evaluated on the classical KTH dataset and the challenging MSR Action Dataset II, which consists of crowded videos with moving people or vehicles in the background. Experiment results demonstrate that the proposed method significantly outperforms existing techniques, especially for action detection in crowded videos.

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