Abstract
In the visual target tracking, the template image often contains background information because of manual box selection, which increases the difficulty of feature extraction and the complexity of calculation when the image goes through feature extraction. The existing method has achieved satisfactory results by optimizing the internal of the Siamese network model. However, it fails to consider pre-processing the template image, which is an important process to improve the performance of the target tracking model. Thus, we use image segmentation to extract the target from the template image and then propose a method that introducing the clustering segmentation into the Siamese network to reduce the background information on the tracker. Introducing our present into SiamFC, SiamRPN, SiamRPN++, and SiamFC++ frameworks, we achieve performance improvements on both OTB2015 and VOT2018 challenging benchmarks.
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Han, X., Qin, Q., Wang, Y., Zhang, Y., Li, H., Liu, Z. (2022). CS-Siam: Siamese-Type Network Tracking Method with Added Cluster Segmentation. In: Li, B., et al. Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13088. Springer, Cham. https://doi.org/10.1007/978-3-030-95408-6_19
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