NDLI: A system based on sequence learning for event detection in surveillance video

Content Provider	IEEE Xplore Digital Library
Author	Xiaoyu Fang Ziwei Xia Chi Su Teng Xu Yonghong Tian Yaowei Wang Tiejun Huang
Copyright Year	2013
Description	Author affiliation: Dept. of Electron. Eng., Beijing Inst. of Technol., Beijing, China (Ziwei Xia; Yaowei Wang) \|\| Sch. of EE&CS, Peking Univ., Beijing, China (Xiaoyu Fang; Chi Su; Teng Xu; Yonghong Tian; Tiejun Huang)
Abstract	Event detection in crowded surveillance videos is a challenging yet important problem. In this paper, we present our eSur (Event detection system on SURveillance video) system, which is derived from TRECVid'12 surveillance tasks. Currently, eSur attempts to detect two categories of events: 1) pair-wise events (e.g., PeopleMeet, PeopleSplitUp and Embrace); 2) action-like events (e.g., ObjectPut, CellToEar, PersonRuns and Pointing). In eSur system, we first employ people detection and tracking algorithms to locate target persons in 3D space-time domain. Then the video sequences in which target persons occur are partitioned into several spatio-temporal cubes. Visual features (i.e. cubic feature and MoSIFT) are computed over these cubes. After that, a sequence learning method, (namely SVM with dynamic time alignment kernel), is employed to infer the existence of an event for the video sequence. According to the TRECVid SED formal evaluation, eSur has yielded fairly encouraging results on TRECVid'12 dataset.
Starting Page	3587
Ending Page	3591
File Size	664200
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738740
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	sequence learning Event detection surveillance
Content Type	Text
Resource Type	Article

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