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Tracking targets in large scale surveillance camera networks

Title
Tracking targets in large scale surveillance camera networks
Funding
ARC | Discovery Projects
Contract (GA) number
DP1094764
Start Date
2010/01/01
End Date
2012/12/31
Open Access mandate
no
Organizations
-
More information
http://purl.org/au-research/grants/arc/DP1094764

 

  • Non-sparse Linear Representations for Visual Tracking with Online Reservoir Metric Learning

    Li, Xi; Shen, Chunhua; Shi, Qinfeng; Dick, Anthony; Hengel, Anton van den (2012)
    Projects: ARC | Tracking targets in large scale surveillance camera networks (DP1094764)
    Most sparse linear representation-based trackers need to solve a computationally expensive L1-regularized optimization problem. To address this problem, we propose a visual tracker based on non-sparse linear representations, which admit an efficient closed-form solution without sacrificing accuracy. Moreover, in order to capture the correlation information between different feature dimensions, we learn a Mahalanobis distance metric in an online fashion and incorporate the learned metric into ...

    Incremental Learning of 3D-DCT Compact Representations for Robust Visual Tracking

    Li, Xi; Dick, Anthony; Shen, Chunhua; Hengel, Anton van den; Wang, Hanzi (2012)
    Projects: ARC | Tracking targets in large scale surveillance camera networks (DP1094764)
    Visual tracking usually requires an object appearance model that is robust to changing illumination, pose and other factors encountered in video. In this paper, we construct an appearance model using the 3D discrete cosine transform (3D-DCT). The 3D-DCT is based on a set of cosine basis functions, which are determined by the dimensions of the 3D signal and thus independent of the input video data. In addition, the 3D-DCT can generate a compact energy spectrum whose high-frequency coefficients...
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