We present a novel off-line algorithm for target segmentation and tracking in video. In our approach, video data is represented by a multi-label Markov Random Field model, and segmentation is accomplished by finding the minimum energy label assignment. We propose a novel energy formulation which incorporates both segmentation and motion estimation in a single framework. Our energy functions enforce motion coherence both within and across frames. We utilize state-of-the-art methods to efficiently optimize over a large number of discrete labels. In addition, we introduce a new ground-truth dataset, called SegTrack, for the evaluation of segmentation accuracy in video tracking. We compare our method with two recent on-line tracking algorithms and provide quantitative and qualitative performance comparisons. © 2010. The copyright of this document resides with its authors.
CITATION STYLE
Tsai, D., Flagg, M., & Rehg, J. M. (2010). Motion coherent tracking with multi-label MRF optimization. In British Machine Vision Conference, BMVC 2010 - Proceedings. British Machine Vision Association, BMVA. https://doi.org/10.5244/C.24.56
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