Moving Object Segmentation
This work is concerned with the development of fast online algorithms for autonomously segmenting moving objects from video
This work addresses the problem of fast, online segmentation of moving objects in video. We pose this as a discriminative on- line semi-supervised appearance learning task, where supervising labels are autonomously generated by a motion segmentation algorithm. The computational complexity of the approach is significantly reduced by performing learning and classification on oversegmented image regions (superpixels), rather than per pixel. In addition, we further exploit the sparse trajectories from the motion segmentation to obtain a simple model that encodes the spatial properties and location of objects at each frame. Fusing these complementary cues produces good object segmentations at very low computational cost. In contrast to previous work, the proposed approach (1) performs segmentation on-the-fly (allowing for applications where data arrives sequentially), (2) has no prior model of object types or ‘objectness’, and (3) operates at significantly reduced computational cost. The approach and its ability to learn, disambiguate and segment the moving objects in the scene is evaluated on a number of benchmark video sequences.
Aside from the results presented in the related publications, there are also some demo videos here:
This project used two existing benchmark datasets:
Last updated: 2020-12-10