›› 2017, Vol. 32 ›› Issue (3): 520-535.doi: 10.1007/s11390-017-1741-z

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

• Special Section of CVM 2017 • Previous Articles     Next Articles

Prior-Free Dependent Motion Segmentation Using Helmholtz-Hodge Decomposition Based Object-Motion Oriented Map

Cui-Cui Zhang1, Zhi-Lei Liu2,*, Member, CCF   

  1. 1. School of Marine Science and Technology, Tianjin University, Tianjin 300072, China;
    2. Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology Tianjin University, Tianjin 300072, China
  • Received:2017-01-19 Revised:2017-04-06 Online:2017-05-05 Published:2017-05-05
  • Contact: Zhi-Lei Liu E-mail:zhileiliu@tju.edu.cn
  • About author:Cui-Cui Zhang received her Ph.D. degree in computer science from the Kyoto University, Kyoto, in 2015. She is currently an assistant professor in the School of Marine Science and Technology, Tianjin University, Tianjin. Her research interests are computer graphics and visualization.
  • Supported by:

    This work is supported by the National Natural Science Foundation of China under Grant No. 61503277.

Motion segmentation in moving camera videos is a very challenging task because of the motion dependence between the camera and moving objects. Camera motion compensation is recognized as an effective approach. However, existing work depends on prior-knowledge on the camera motion and scene structure for model selection. This is not always available in practice. Moreover, the image plane motion suffers from depth variations, which leads to depth-dependent motion segmentation in 3D scenes. To solve these problems, this paper develops a prior-free dependent motion segmentation algorithm by introducing a modified Helmholtz-Hodge decomposition (HHD) based object-motion oriented map (OOM). By decomposing the image motion (optical flow) into a curl-free and a divergence-free component, all kinds of camera-induced image motions can be represented by these two components in an invariant way. HHD identifies the camera-induced image motion as one segment irrespective of depth variations with the help of OOM. To segment object motions from the scene, we deploy a novel spatio-temporal constrained quadtree labeling. Extensive experimental results on benchmarks demonstrate that our method improves the performance of the state-of-the-art by 10%~20% even over challenging scenes with complex background.

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