Zhongwang Cai
ABSTRACT
Considering the peculiarity of SiamCAR, which decomposes visual tracking into a Siamese subnetwork for feature extraction and a classification-regression subnetwork for bounding box prediction, we propose an empirical method to avoid cropping the high-level semantic information for further anchor-free tracking tasks. Based on the preprocessing of the training image pairs, we focus on the center region of the feature maps in the template branch, which includes the whole object while reducing background clutter. However, the position of the object in a sequence is always in constant motion. The loss of high-level features causes the large movement of the object to weaken the performance of the strategy for tracking the current object in accordance with the object location of the previous frame. Therefore, we remove the inappropriate cuts to obtain a better similarity map. Extensive experiments and comparisons show that our proposed simple but effective method achieves credible results with remarkable real-time speed on the UAV123, LaSOT and GOT-10k benchmarks.
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Index Terms
- Uncropped Siamese Fully Convolutional Network for Visual Tracking
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