2019/11/17 by Dongyan Guo, Jun Wang, Guo, Dongyan +7 · 41 citations
Computer Science · Engineering · Mathematics · #Algorithm #Artificial intelligence #BitTorrent tracker #Bounding overwatch #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Convolutional neural network #Eye tracking #FOS: Computer and information sciences #Feature (linguistics) #Feature extraction #Fire Detection and Safety Systems #Image (mathematics) #Image Enhancement Techniques #Mathematics #Minimum bounding box #Pattern recognition (psychology) #Pixel #Rank (graph theory) #Regression #Simple (philosophy) #Sorting #Statistics #Subnetwork #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.1911.07241
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2019/11/17 · openalex created_date 2019/11/22 · arxiv created 2019/12/13 · arxiv updated 2019/12/16 · openalex updated_date 2026/08/08
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 GOT-10K, LaSOT, UAV123 and OTB-50. Without bells and whistles, our SiamCAR achieves the leading performance with a considerable real-time speed.