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Here, we propose a novel fully convolutional Siamese network to solve visual tracking by directly predicting the target bounding box in an end-to-end manner. We first reformulate the visual tracking task as two subproblems: a classification problem for pixel category prediction and a regression task for object status estimation at this pixel. With this decomposition, we design a simple yet effective Siamese architecture based classification and regression framework, termed SiamCAR, which consists of two subnetworks: a Siamese subnetwork for feature extraction and a classification-regression subnetwork for direct bounding box prediction. Since the proposed framework is both proposal- and anchor-free, SiamCAR can avoid the tedious hyper-parameter tuning of anchors, considerably simplifying the training. To demonstrate that a much simpler tracking framework can achieve superior tracking results, we conduct extensive experiments and comparisons with state-of-the-art trackers on a few challenging benchmarks. Without bells and whistles, SiamCAR achieves leading performance with a real-time speed. Furthermore, the ablation study validates that the proposed framework is effective with various backbone networks, and can benefit from deeper networks. Code is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/ohhhyeahhh\/SiamCAR\">https:\/\/github.com\/ohhhyeahhh\/SiamCAR<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s11263-021-01559-4","type":"journal-article","created":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T20:02:21Z","timestamp":1641499341000},"page":"550-566","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Joint Classification and Regression for Visual Tracking with Fully Convolutional Siamese Networks"],"prefix":"10.1007","volume":"130","author":[{"given":"Ying","family":"Cui","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9811-4828","authenticated-orcid":false,"given":"Dongyan","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Yanyan","family":"Shao","sequence":"additional","affiliation":[]},{"given":"Zhenhua","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Chunhua","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Liyan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Shengyong","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,6]]},"reference":[{"key":"1559_CR1","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J., Vedaldi, A., & Torr, P. 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