Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 27, Issue 6, Pages 2688-2700Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2018.2795740
Keywords
Submodular maximization; trajectory; motion segmentation
Funding
- Beijing Natural Science Foundation [4182056]
- National Key R&D Program of China [2017YFC0112000]
- National Natural Science Foundation of China [61272359, 61672099, 81627803]
- Fok Ying-Tong Education Foundation for Young Teachers
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We propose a new trajectory clustering method using submodular optimization for better motion segmentation in videos. A small number of representative trajectories are first selected by submodular maximization automatically. Then all the initial trajectories can be segmented into fragments with the representative trajectories as centers of fragments. At last, fragments are merged into clusters by a two-stage bottom-up clustering method, and each cluster shows the motion of one moving object. The submodular energy function integrates the quality of all trajectories and their correlations. As a result, thousands of initial trajectories are replaced by only dozens of representative trajectories, which will reduce the negative influence of inaccurate initial trajectories on motion segmentation. The representative trajectories will have larger weights while extracting color or texture information of each moving entity at the step of motion segmentation. Experimental results demonstrate that our method can divide trajectories into more accurate clusters. The final motion segmentation results also illustrate that our method outperforms state-of-the-art motion segmentation methods based on trajectory clustering.(1)
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