  Human video instance segmentation plays an important role in computer
understanding of human activities and is widely used in video processing, video
surveillance, and human modeling in virtual reality. Most current VIS methods
are based on Mask-RCNN framework, where the target appearance and motion
information for data matching will increase computational cost and have an
impact on segmentation real-time performance; on the other hand, the existing
datasets for VIS focus less on all the people appearing in the video. In this
paper, to solve the problems, we develop a new method for human video instance
segmentation based on single-stage detector. To tracking the instance across
the video, we have adopted data association strategy for matching the same
instance in the video sequence, where we jointly learn target instance
appearances and their affinities in a pair of video frames in an end-to-end
fashion. We have also adopted the centroid sampling strategy for enhancing the
embedding extraction ability of instance, which is to bias the instance
position to the inside of each instance mask with heavy overlap condition. As a
result, even there exists a sudden change in the character activity, the
instance position will not move out of the mask, so that the problem that the
same instance is represented by two different instances can be alleviated.
Finally, we collect PVIS dataset by assembling several video instance
segmentation datasets to fill the gap of the current lack of datasets dedicated
to human video segmentation. Extensive simulations based on such dataset has
been conduct. Simulation results verify the effectiveness and efficiency of the
proposed work.
