  Crowd segmentation is a fundamental task serving as the basis of crowded
scene analysis, and it is highly desirable to obtain refined pixel-level
segmentation maps. However, it remains a challenging problem, as existing
approaches either require dense pixel-level annotations to train deep learning
models or merely produce rough segmentation maps from optical or particle flows
with physical models. In this paper, we propose the Motion Prior-Aware Siamese
Network (MPASNET) for unsupervised crowd semantic segmentation. This model not
only eliminates the need for annotation but also yields high-quality
segmentation maps. Specially, we first analyze the coherent motion patterns
across the frames and then apply a circular region merging strategy on the
collective particles to generate pseudo-labels. Moreover, we equip MPASNET with
siamese branches for augmentation-invariant regularization and siamese feature
aggregation. Experiments over benchmark datasets indicate that our model
outperforms the state-of-the-arts by more than 12% in terms of mIoU.
