Fast Video Object Segmentation With Temporal Aggregation Network and Dynamic Template Matching
Significant progress has been made in Video Object Segmentation (VOS) the video object tracking task in its finest level. While the VOS task can be naturally decoupled into image semantic segmentation and video object tracking, significantly much more research effort has been made in segmentation than tracking. In this paper, we introduce "tracking by detection" into VOS which can coherently integrates segmentation into tracking, by proposing a new temporal aggregation network and a novel dynamic time evolving template matching mechanism to achieve significantly improved performance. Notably, our method is entirely online and thus suitable for one shot learning, and our end to end trainable model allows multiple object segmentation in one forward pass. We achieve new state of the art performance on the DAVIS benchmark without complicated bells and whistles in both speed and accuracy, with a speed of 0.14 second per frame and J &F measure of 75.9% respectively.
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)