近日，2020年国际计算机视觉与模式识别会议（CVPR2020）论文录用名单发布，我院计算机视觉研究所教师郭东岩、崔滢、王振华、博士研究生王俊和特聘教授陈胜勇在目标跟踪方向的论文“SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking”被该会议录用。这也是我院首次作为第一完成单位在该会议上发表论文。该文提出了一种新型的全卷积孪生网络模型SiamCAR实现端到端的目标跟踪，网络结构简洁高效，在多个具有挑战性的数据集上取得了领先的效果。 

CVPR 的全称是 IEEE Conference on Computer Vision and Pattern Recognition，即国际计算机视觉与模式识别大会，被中国计算机学会（CCF）列为A类会议。CVPR由IEEE举办，与国际计算机视觉会议（ICCV）和欧洲计算机视觉会议（ECCV）并称计算机视觉领域的三大顶级会议。2020年CVPR将于6月16日至6月20日在美国西雅图举办。

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题目：SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking

作者：Dongyan Guo, Jun Wang, Ying Cui, Zhenhua Wang, Shengyong Chen

Abstract

By decomposing the visual tracking task into two subproblems as classification for pixel category and regression for object bounding box at this pixel, we propose a novel fully convolutional Siamese network to solve visual tracking end-to-end in a per-pixel manner. The proposed framework SiamCAR consists of two simple subnetworks: one Siamese subnetwork for feature extraction and one classification-regression subnetwork for bounding box prediction. Our framework takes ResNet-50 as backbone. Different from state-of-the-art trackers like Siamese-RPN, SiamRPN++ and SPM, which are based on region proposal, the proposed framework is both proposal and anchor free. Consequently, we are able to avoid the tricky hyper-parameter tuning of anchors and reduce human intervention. The proposed framework is simple, neat and effective. Extensive experiments and comparisons with state-of-the-art trackers are conducted on many challenging benchmarks like GOT10K, LaSOT, UAV123 and OTB-50. Without bells and whistles, our SiamCAR achieves the leading performance with a considerable real-time speed.