期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 42, 期 1, 页码 140-153出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2018.2876253
关键词
Trajectory; Motion segmentation; Computer vision; Correlation; Object tracking; Clustering algorithms; Computer vision; video analysis; motion; segmentation; tracking; correlation clustering
资金
- ERC Starting Grant VideoLearn
- DFG [KE 2264/1-1]
Models for computer vision are commonly defined either w.r.t. low-level concepts such as pixels that are to be grouped, or w.r.t. high-level concepts such as semantic objects that are to be detected and tracked. Combining bottom-up grouping with top-down detection and tracking, although highly desirable, is a challenging problem. We state this joint problem as a co-clustering problem that is principled and tractable by existing algorithms. We demonstrate the effectiveness of this approach by combining bottom-up motion segmentation by grouping of point trajectories with high-level multiple object tracking by clustering of bounding boxes. We show that solving the joint problem is beneficial at the low-level, in terms of the FBMS59 motion segmentation benchmark, and at the high-level, in terms of the Multiple Object Tracking benchmarks MOT15, MOT16, and the MOT17 challenge, and is state-of-the-art in some metrics.
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