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access icon openaccess Robust visual tracking via two-stage binocular sparse learning

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 19/07/2018, Accepted 31/07/2018, Published 16/08/2018

Combining multiple features and enforcing joint sparsity have proven to be beneficial for robust tracking. In this study, a novel stereo vision and two-stage sparse representation-based method is presented. First, the colouring information-based features are augmented with a depth view in the appearance modelling of a target object. Unreliable features are then dynamically removed for robust feature-level fusion in the first stage of sparse optimisation. Next, the low rank constraint is imposed onto the objective function, which facilitates a more robust representation of the ensemble of particles over the pruned views. Finally, the authors propose to detect occlusion via depth-based histogram analysis to guarantee the effectiveness of the template update. Experiments are performed on two large-scale benchmark datasets: KITTI and Princeton. Authors’ approach achieves state-of-the-art results in the aspect of robustness and accuracy.

Inspec keywords: object tracking; image fusion; learning (artificial intelligence); feature extraction; image representation; stereo image processing

Other keywords: two-stage sparse representation-based method; robustness; enforcing joint sparsity; robust visual tracking; novel stereo vision; robust feature-level fusion; depth view; colouring information-based features; two-stage binocular sparse learning; sparse optimisation; robust representation; robust tracking; pruned views; unreliable features; low rank constraint; target object; multiple features; depth-based histogram analysis; objective function; appearance modelling

Subjects: Computer vision and image processing techniques; Optical, image and video signal processing

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