Papers › A Twofold Siamese Network for Real-Time Object Tracking
A Twofold Siamese Network for Real-Time Object Tracking
Anfeng He, Chong Luo, Xinmei Tian, Wen-Jun Zeng
Observing that Semantic features learned in an image classification task and Appearance features learned in a similarity matching task complement each other, we build a twofold Siamese network, named SA-Siam, for real-time object tracking. SA-Siam is composed of a semantic branch and an appearance branch. Each branch is a similarity-learning Siamese network. An important design choice in SA-Siam is to separately train the two branches to keep the heterogeneity of the two types of features. In addition, we propose a channel attention mechanism for the semantic branch. Channel-wise weights are computed according to the channel activations around the target position. While the inherited architecture from SiamFC \cite{SiamFC} allows our tracker to operate beyond real-time, the twofold design and the attention mechanism significantly improve the tracking performance. The proposed SA-Siam outperforms all other real-time trackers by a large margin on OTB-2013/50/100 benchmarks.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Visual Object Tracking | OTB-2013 | SA-Siam | AUC | 0.677 | #2 of 7 | Archive leaderboard | report |
| Visual Object Tracking | OTB-2015 | SA-Siam | AUC | 0.657 | #14 of 18 | Archive leaderboard | report |
| Visual Object Tracking | OTB-50 | SA-Siam | AUC | 0.610 | #2 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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