Submodular Trajectories for Better Motion Segmentation in Videos

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.
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Our source code is available at https://github.com/shenjianbing/submodularmotion.