Motion Coherent Tracking Using Multi-label MRF Optimization
- First Online:
- Cite this article as:
- Tsai, D., Flagg, M., Nakazawa, A. et al. Int J Comput Vis (2012) 100: 190. doi:10.1007/s11263-011-0512-5
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 Georgia Tech Segmentation and Tracking Dataset (GT-SegTrack), for the evaluation of segmentation accuracy in video tracking. We compare our method with several recent on-line tracking algorithms and provide quantitative and qualitative performance comparisons.
KeywordsVideo object segmentation Visual tracking Markov random field Motion coherence Combinatoric optimization Biotracking
Unable to display preview. Download preview PDF.
(AVI 10.8 MB)