Multiple Kernelized Correlation Filters (MKCF) for Extended Object Tracking Using X-Band Marine Radar Data

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Authors Yi Zhou, T. Wang, Ronghua Hu, Hangsheng Su, Yi Liu, Xiaoming Liu, Jidong Suo, H. Snoussi
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Paper Abstract Conventional tracking filter in marine radar only concerns the position or shape of the object. When the target encounters the clutter interruption or ship occlusion, the tracker is easy to drift away due to the wrong target association. Since in short-range maritime surveillance, the X-band marine radar captures the object in an extended region with varying intensities, in this paper, combining position, shape, and appearance of the target together, multiple kernelized correlation filters (MKCF) are proposed to conduct a single object tracking in the real marine radar. By automatic initializing KCF on a target in different time steps and fusing these multiple KCFs via the maximum likelihood, the proposed tracker implicitly uses multiple instances of the target to improve the robustness of the long-term tracking. Bounding rectangles of the multiple trackers also enhance the reliability of ship segmentation via a voting procedure. In the real ship tracking experiment, the proposed tracker performs favorably against the conventional radar tracker and top-ranked visual tracker in the cases of clutter interruption, ship occlusions, and scale changing. We believe that our proposal sheds light on that the intensity distribution of the extended object could be valuable for target association in marine radar data. To encourage further research, our tracking framework is made open-source.
Date of publication 2019
Code Programming Language Python

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