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CVPR
2009
IEEE

Learning color and locality cues for moving object detection and segmentation

10 years 3 months ago
Learning color and locality cues for moving object detection and segmentation
This paper presents an algorithm for automatically detecting and segmenting a moving object from a monocular video. Detecting and segmenting a moving object from a video with limited object motion is challenging. Since existing automatic algorithms rely on motion to detect the moving object, they cannot work well when the object motion is sparse and insufficient. In this paper, we present an unsupervised algorithm to learn object color and locality cues from the sparse motion information. We first detect key frames with reliable motion cues and then estimate moving sub-objects based on these motion cues using a Markov Random Field (MRF) framework. From these sub-objects, we learn an appearance model as a color Gaussian Mixture Model. To avoid the false classification of background pixels with similar color to the moving objects, the locations of these sub-objects are propagated to neighboring frames as locality cues. Finally, robust moving object segmentation is achieved by combining t...
Feng Liu (University of Wisconsin-Madison), Michae
Added 09 May 2009
Updated 10 Dec 2009
Type Conference
Year 2009
Where CVPR
Authors Feng Liu (University of Wisconsin-Madison), Michael Gleicher (University of Wisconsin-Madison)
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