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Coupled-layer based visual tracking via adaptive kernelized correlation filters
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DOI：10.1007/s00371-016-1310-4.png)
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Abstract
En 中文
Part-based visual model is particularly useful when the target appearance undergoes partial occlusion or deformation. The existing reliable patches tracking (RPT) method has achieved better result by identifying and exploiting the reliable patches that can be tracked correctly, yet it tends to fail in some challenging scenes since it ignores the holistic information of target completely, while, in fact, the target's holistic appearance provides more discriminative features than local patches with low resolution. Based on the existing RPT and kernelized correlation filters tracking method, in this paper, we propose a coupled-layer visual model based tracker by combining the target's global and local appearance in a coupled way. The global layer provides the holistic information and is treated as an approximation of the target. The local layer is composed of multiple small patches that are randomly initialized in the first frame. During tracking, the global tracker detects the target itself; its detection result is employed in the local layer to exploit the reliable patches and to estimate the target position corresponding to each patch. The exploited reliable patches are employed to estimate the target scale and to vote the current target location. Finally, both global and local models are updated with carefully designed updating mechanisms. Experiments conducted on 80 challenging benchmark sequences clearly show that our tracker improves the RPT tracker significantly both in overall and individual performance yet without obvious speed cost. Also, our tracker outperforms all the state-of-the-art trackers in overall datasets and eight independent datasets.
Keywords:
Coupled-layer visual model
Reliable patches
Kernelized correlation filters
Visual tracking
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