NDLI: Multi-camera spatio-temporal fusion and biased sequence-data learning for security surveillance

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Multi-camera spatio-temporal fusion and biased sequence-data learning for security surveillance

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Multi-camera spatio-temporal fusion and biased sequence-data learning for security surveillance

Content Provider	ACM Digital Library
Author	Jiao, Long Wu, Gang Chang, Edward Y. Wu, Yi Wang, Yuan-Fang
Abstract	We present a framework for multi-camera video surveillance. The framework consists of three phases: detection, representation, and recognition. The detection phase handles multi-source spatio-temporal data fusion for efficiently and reliably extracting motion trajectories from video. The representation phase summarizes raw trajectory data to construct hierarchical, invariant, and content-rich descriptions of the motion events. Finally, the recognition phase deals with event classification and identification on the data descriptors. Because of space limits, we describe only briefly how we detect and represent events, but we provide in-depth treatment on the third phase: event recognition. For effective recognition, we devise a sequence-alignment kernel function to perform sequence data learning for identifying suspicious events. We show that when the positive training instances (i.e., suspicious events) are significantly outnumbered by the negative training instances (benign events), then SVMs (or any other learning methods) can suffer a high incidence of errors. To remedy this problem, we propose the kernel boundary alignment (KBA) algorithm to work with the sequence-alignment kernel. Through empirical study in a parking-lot surveillance setting, we show that our spatio-temporal fusion scheme and biased sequence-data learning method are highly effective in identifying suspicious events.
Starting Page	528
Ending Page	538
Page Count	11
File Format	PDF
ISBN	1581137222
DOI	10.1145/957013.957126
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2003-11-02
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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