Abstract:
Visual tracking algorithm based on a Siamese network is an important method in the field of visual tracking in recent years, and it has good performance in tracking speed and accuracy. However, most tracking algorithms based on the Siamese network rely on an off-line training model and lack of online update to tracker. In order to solve this problem, we propose an online learning-based visual tracking algorithm for Siamese networks. The algorithm adopts the idea of double template, treats the target in the first frame as a static template, and uses the high confidence update strategy to obtain the dynamic template in the subsequent frame; in online tracking, the fast transform learning model is used to learn the apparent changes of the target from the double template, and the target likelihood probability map of the search area is calculated according to the color histogram characteristics of the current frame, and the background suppression learning is carried out. Finally, the response map obtained by the dual templates is weighted, and the final prediction result is obtained. The experimental results on OTB2015, TempleColor128, and VOT datasets show that the test results of this algorithm are improved compared with the mainstream algorithms in recent years and have better tracking performance in target deformation, similar background interference, fast motion, and other scenarios.
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Abstract: Visual tracking algorithm based on a Siamese network is an important method in the field of visual tracking in recent years, and it has good performance in tracking speed and accuracy. However, most tracking algorithms based on the Siamese network rely on an off-line training model and lack of online update to tracker. In order to solve this problem, we propose an online learning-based visual tracking algorithm for Siamese networks. The algorithm adopts the idea of double template, treats the target in the first frame as a static template, and uses the high confidence update strategy to obtain the dynamic template in the subsequent frame; in online tracking, the fast transform learning model is used to learn the apparent changes of the target from the double template, and the target likelihood probability map of the search area is calculated according to the color histogram characteristics of the current frame, and the background suppression learning is carried out. Finally, the response map obtained by the dual templates is weighted, and the final prediction result is obtained. The experimental results on OTB2015, TempleColor128, and VOT datasets show that the test results of this algorithm are improved compared with the mainstream algorithms in recent years and have better tracking performance in target deformation, similar background interference, fast motion, and other scenarios.
