Towards Accurate Pixel-wise Object Tracking by Attention Retrieval

Understand

The encoding of the target in object tracking moves from the coarse bounding-box to fine-grained segmentation map recently.

• Revisiting de facto real-time approaches that are capable of predicting mask during tracking, we observed that they usually fork a light branch from the backbone network for segmentation.
• Although efficient, directly fusing backbone features without considering the negative influence of background clutter tends to introduce false-negative predictions, lagging the segmentation accuracy.
• To mitigate this problem, we propose an attention retrieval network (ARN) to perform soft spatial constraints on backbone features.