Multiframe Many-Many Point Correspondence for Vehicle Tracking in High Density Wide Area Aerial Videos

    Authors

    I. Saleemi;M. Shah

    Comments

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    Abbreviated Journal Title

    Int. J. Comput. Vis.

    Keywords

    Wide area aerial surveillance; Multi target tracking; Multiframe data; association; Multiple hypothesis; Multiple candidate tracking; CLIF; dataset; ALGORITHM; ASSIGNMENTS; Computer Science, Artificial Intelligence

    Abstract

    This paper presents a novel framework for tracking thousands of vehicles in high resolution, low frame rate, multiple camera aerial videos. The proposed algorithm avoids the pitfalls of global minimization of data association costs and instead maintains multiple object-centric associations for each track. Representation of object state in terms of many to many data associations per track is proposed and multiple novel constraints are introduced to make the association problem tractable while allowing sharing of detections among tracks. Weighted hypothetical measurements are introduced to better handle occlusions, mis-detections and split or merged detections. A two-frame differencing method is presented which performs simultaneous moving object detection in both. Two novel contextual constraints of vehicle following model, and discouragement of track intersection and merging are also proposed. Extensive experiments on challenging, ground truthed data sets are performed to show the feasibility and superiority of the proposed approach. Results of quantitative comparison with existing approaches are presented, and the efficacy of newly introduced constraints is experimentally established. The proposed algorithm performs better and faster than global, 1-1 data association methods.

    Journal Title

    International Journal of Computer Vision

    Volume

    104

    Issue/Number

    2

    Publication Date

    1-1-2013

    Document Type

    Article

    Language

    English

    First Page

    198

    Last Page

    219

    WOS Identifier

    WOS:000322251800005

    ISSN

    0920-5691

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