Automatic Fence Segmentation in Videos of Dynamic Scenes

Renjiao Yi, Jue Wang, Ping Tan; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 705-713

Abstract


We present a fully automatic approach to detect and segment fence-like occluders from a video clip. Unlike previous approaches that usually assume either static scenes or cameras, our method is capable of handling both dynamic scenes and moving cameras. Under a bottom-up framework, it first clusters pixels into coherent groups using color and motion features. These pixel groups are then analyzed in a fully connected graph, and labeled as either fence or non-fence using graph-cut optimization. Finally, we solve a dense Conditional Random Filed (CRF) constructed from multiple frames to enhance both spatial accuracy and temporal coherence of the segmentation. Once segmented, one can use existing hole-filling methods to generate a fence-free output. Extensive evaluation suggests that our method outperforms previous automatic and interactive approaches on complex examples captured by mobile devices.

Related Material


[pdf]
[bibtex]
@InProceedings{Yi_2016_CVPR,
author = {Yi, Renjiao and Wang, Jue and Tan, Ping},
title = {Automatic Fence Segmentation in Videos of Dynamic Scenes},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}