Name
Title | ||
|---|---|---|
MPASNET - Motion Prior-Aware Siamese Network For Unsupervised Deep Crowd Segmentation In Video Scenes. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICIP42928.2021.9506675 | ICIP |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinhai Yang | 1 | 0 | 1.01 |
| Hua Yang | 2 | 16 | 8.81 |