We introduce a semi-supervised video segmentation approach based on an efficient video representation, called as 'super-trajectory'. A super-trajectory corresponds to a group of compact point trajectories that exhibit consistent motion patterns, similar appearances, and close spatiotemporal relationships. We generate the compact trajectories using a probabilistic model, which enables handling of occlusions and drifts effectively. To reliably group point trajectories, we adopt the density peaks based clustering algorithm that allows capturing rich spatiotemporal relations among trajectories in the clustering process. We incorporate two intuitive mechanisms for segmentation, called as reverse-tracking and object re-occurrence, for robustness and boosting the performance. Building on the proposed video representation, our segmentation method is discriminative enough to accurately propagate the initial annotations in the first frame onto the remaining frames. Our extensive experimental analyses on three challenging benchmarks demonstrate that, our method is capable of extracting the target objects from complex backgrounds, and even reidentifying them after prolonged occlusions, producing high-quality video object segments. The code and results are available at: https://github.com/wenguanwang/SupertrajectorySeg.
|Number of pages
|IEEE Transactions on Pattern Analysis and Machine Intelligence
|Published - Apr 1 2019
Bibliographical noteFunding Information:
This work was supported in part by the Beijing Natural Science Foundation under Grant 4182056, the National Basic Research Program of China under grant 2013CB328805, the Australian Research Council’s Discovery Projects funding scheme under grant DP150104645, and the Fok Ying-Tong Education Foundation for Young Teachers. Specialized Fund for Joint Building Program of Beijing Municipal Education Commission.
© 1979-2012 IEEE.
- Video segmentation
- density peaks clustering
- trajectory extraction
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
- Artificial Intelligence
- Applied Mathematics