Coupled ensemble graph cuts and object verification for animal segmentation from highly cluttered videos
In this paper, we consider animal object segmentation from wildlife monitoring videos captured by motion-triggered cameras, called camera-traps. This is a very challenging task because the wildlife monitoring scenes are often highly cluttered and dynamic. To address this issue, we propose to explore the ideas of coupled ensemble graph cuts and object verification. We consider video object cut as an ensemble of frame-level background-foreground object classifiers which fuse information across frames and refine their segmentation results in a collaborative and iterative manner. To significantly reduce false positives in foreground animal detection and segmentation, we learn an object verification model to further classify if the segmented image patch belongs to the background or the animal. Our extensive experimental results and performance comparisons over a diverse set of challenging camera-trap data, as well as the new Change Detection 2014 benchmark dataset, demonstrate that the proposed framework outperforms various state-of-the-art algorithms and has the capability to handle even the most challenging objects in a wide variety of video sequences.