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    Tracking vehicles as groups in airborne videos

    Cao, X. and Shi, Z. and Yan, P. and Li, Xuelong (2013) Tracking vehicles as groups in airborne videos. Neurocomputing 99 , pp. 38-45. ISSN 0925-2312.

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    Abstract

    Airborne vehicle tracking system is receiving increasing attention due to its high mobility, low cost and large surveillance scope. However, tracking multiple vehicles simultaneously on airborne platform is a challenging problem, owing to camera vibration, which causes visible frame-to-frame jitter in the airborne videos and uncertain vehicle motion. To address these problems, a new collaborative tracking framework is proposed in this paper. The framework consists of a two-level tracking process to track vehicles as groups. The higher level builds the relevance network and divides target vehicles into different groups, where the relevance is calculated based on the status information of vehicles obtained from the lower level. The proposed group tracking takes into account the relevance between vehicles and reduces the impact of camera vibration. Experimental results demonstrated that the proposed method has better performance in terms of tracking speed and tracking accuracy compared to other existing approaches based on particle filter and stationary grouping.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): group tracking, relevance network, Kalman filter, airborne platforms, multi-target tracking
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 06 Jun 2013 10:10
    Last Modified: 11 Oct 2016 15:27
    URI: https://eprints.bbk.ac.uk/id/eprint/7295

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