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    ASIAIR > College of Computer Science > Proceedings >  Item 310904400/7152

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    Title: A Framework of Spatio-Temporal Analysis for Video Surveillance
    Authors: Duan-Yu Chen;Kevin Cannons;Hsiao-Rong Tyan;Sheng-Wen Shih;Hong-Yuan Mark Liao
    Contributors: Institute of Information Science, Academia Sinica, Taiwan;Department of Computer Science and Engineering, York University, Canada;Department of Information and Computer Engineering, Chung Yuan Christian University, Taiwan;Department of Computer Science and Information Engineering;Institute of Information Science, Academia Sinica, Taiwan
    Keywords: video surveillance;object classification;spatiotemporal analysis
    Date: 2007-12-20
    Issue Date: 2010-01-12 16:23:31 (UTC+8)
    Publisher: 亞洲大學資訊學院;中華電腦學會
    Abstract: This paper presents a video surveillance system that is capable of detecting and classifying moving targets in real-time. The system extracts moving targets from a video stream and classifies them into predefined categories according to their spatiotemporal properties. Classification of the moving targets is completed via a combination of a temporal boosted classifier and spatiotemporal “motion energy” analysis. We illustrate that a temporal boosted classifier can be designed that successfully recognizes five object categories: person(s), bicycle, motorcycle, vehicle, and person with umbrella. The proposed temporal boosted classifier has the unique ability to improve weak classifiers by allowing them to make use of previous information when evaluating the current frame. In addition, we demonstrate a method to further process targets in the “person(s)” category to determine if they are single moving individuals or crowds. It is shown that this challenging task of moving crowd recognition can be effectively performed using spatiotemporal motion energies. These motion energies provide a rich description of a target’s dynamic characteristics, from which classification can be performed. Our empirical evaluations demonstrate that the proposed system is extremely effective at recognizing all predefined object classes.
    Relation: 2007NCS全國計算機會議 12-20~21
    Appears in Collections:[College of Computer Science] Proceedings

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