Name
Title | ||
|---|---|---|
Siamcar: Siamese Fully Convolutional Classification And Regression For Visual Tracking |
Abstract | ||
|---|---|---|
By decomposing the visual tracking task into two sub-problems 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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CVPR42600.2020.00630 | 2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) |
DocType | ISSN | Citations |
Conference | 1063-6919 | 6 |
PageRank | References | Authors |
0.42 | 20 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongyan Guo | 1 | 24 | 6.49 |
| Wang Jun | 2 | 6 | 0.42 |
| Ying Cui | 3 | 30 | 6.80 |
| Zhenhua Wang | 4 | 12 | 3.23 |
| Sheng-Yong Chen | 5 | 1077 | 114.06 |