Leveraging Attention for Improved Discriminative Correlation Filters in Single Object Tracking
Ahmed O. Elsaid,
Mohamed M. Fouad,
Tarek S. Ghoniemy
Abstract:Correlation Filter-based Trackers have shown impressive results in the object tracking research, outperforming classical trackers in several benchmarks. However, accurately tracking objects with deformation, fast motion, or occlusion remains a main challenge in the process of tracking. The cyclic suggestion of training samples used in correlation filter tracking usually lead to undesirable boundary effects, that significantly reduce the tracking efficiency. To address this issues, a hybrid attention-based corr… Show more
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