A Twofold Siamese Network for Real-Time Object Tracking

نویسندگان

  • Anfeng He
  • Chong Luo
  • Xinmei Tian
  • Wenjun Zeng
چکیده

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 similaritylearning 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 [3] 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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عنوان ژورنال:
  • CoRR

دوره abs/1802.08817  شماره 

صفحات  -

تاریخ انتشار 2018