Robust Visual Tracking with Channel Attention and Focal Loss

Abstract Recently, the tracking community leads a fashion of end-to-end feature representation learning for visual tracking. Previous works treat all feature channels and training samples equally during training. This ignores channel interdependencies and foreground-background data imbalance, thus limiting the tracking performance. To tackle these problems, we introduce channel attention and focal loss into the network design to enhance feature representation learning. Specifically, a Squeeze-and-Excitation (SE) block is coupled to each convolutional layer to generate channel attention. Channel attention reflects the channel-wise importance of each feature channel and is used for feature weighting in online tracking. To alleviate the foreground-background data imbalance, we propose a focal logistic loss by adding a modulating factor to the logistic loss, with two tunable focusing parameters. The focal logistic loss down-weights the loss assigned to easy examples in the background area. Both the SE block and focal logistic loss are computationally lightweight and impose only a slight increase in model complexity. Extensive experiments are performed on three challenging tracking datasets including OTB100, UAV123 and TC128. Experimental results demonstrate that the enhanced tracker achieves significant performance improvement while running at a real-time frame-rate of 66 fps.