SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking
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. 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 challenging benchmarks including GOT 10K, LaSOT, UAV123 and OTB 50. Without bells and whistles, our SiamCAR achieves the leading performance with a considerable real time speed. The code is available at https:/github.com/ohhhyeahhh/SiamCAR.
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)