  Most existing video tasks related to "human" focus on the segmentation of
salient humans, ignoring the unspecified others in the video. Few studies have
focused on segmenting and tracking all humans in a complex video, including
pedestrians and humans of other states (e.g., seated, riding, or occluded). In
this paper, we propose a novel framework, abbreviated as HVISNet, that segments
and tracks all presented people in given videos based on a one-stage detector.
To better evaluate complex scenes, we offer a new benchmark called HVIS (Human
Video Instance Segmentation), which comprises 1447 human instance masks in 805
high-resolution videos in diverse scenes. Extensive experiments show that our
proposed HVISNet outperforms the state-of-the-art methods in terms of accuracy
at a real-time inference speed (30 FPS), especially on complex video scenes. We
also notice that using the center of the bounding box to distinguish different
individuals severely deteriorates the segmentation accuracy, especially in
heavily occluded conditions. This common phenomenon is referred to as the
ambiguous positive samples problem. To alleviate this problem, we propose a
mechanism named Inner Center Sampling to improve the accuracy of instance
segmentation. Such a plug-and-play inner center sampling mechanism can be
incorporated in any instance segmentation models based on a one-stage detector
to improve the performance. In particular, it gains 4.1 mAP improvement on the
state-of-the-art method in the case of occluded humans. Code and data are
available at https://github.com/IIGROUP/HVISNet.
