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Title: Video event detection : from subvolume localization to spatio-temporal path search
Authors: Tran, Du
Yuan, Junsong
Forsyth, David
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2014
Source: Tran, D., Yuan, J., & Forsyth, D. (2014). Video Event Detection: From Subvolume Localization to Spatiotemporal Path Search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(2), 404-416.
Series/Report no.: IEEE transactions on pattern analysis and machine intelligence
Abstract: Although sliding window-based approaches have been quite successful in detecting objects in images, it is not a trivial problem to extend them to detecting events in videos. We propose to search for spatio-temporal paths for video event detection. This new formulation can accurately detect and locate video events in cluttered and crowded scenes, and is robust to camera motions. It can also well handle the scale, shape, and intra-class variations of the event. Compared to event detection using spatio-temporal sliding windows, the spatio-temporal paths correspond to the event trajectories in the video space, thus can better handle events composed by moving objects. We prove that the proposed search algorithm can achieve the global optimal solution with the lowest complexity. Experiments are conducted on realistic video datasets with different event detection tasks, such as anomaly event detection, walking person detection, and running detection. Our proposed method is compatible to different types of video features or object detectors and robust to false and missed local detections. It significantly improves the overall detection and localization accuracy over the state-of-the-art methods.
ISSN: 0162-8828
DOI: 10.1109/TPAMI.2013.137
Rights: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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