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Communication Dans Un Congrès Année : 2014

On Modeling Ego-Motion Uncertainty for Moving Object Detection from a Mobile Platform

Résumé

In this paper, we propose an effective approach for moving object detection based on modeling the ego-motion uncertainty and using a graph-cut based motion segmentation. First, the relative camera pose is estimated by minimizing the sum of reprojection errors and its covariance matrix is calculated using a first-order errors propagation method. Next, a motion likelihood for each pixel is obtained by propagating the uncertainty of the ego-motion to the Residual Image Motion Flow (RIMF). Finally, the motion likelihood and the depth gradient are used in a graph-cut based approach as region and boundary terms respectively, in order to obtain the moving objects segmentation. Experimental results on real-world data show that our approach can detect dynamic objects which move on the epipolar plane or that are partially occluded in complex urban traffic scenes.
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Dates et versions

hal-01010997 , version 1 (21-06-2014)

Identifiants

  • HAL Id : hal-01010997 , version 1

Citer

Dingfu Zhou, Vincent Fremont, Benjamin Quost, Bihao Wang. On Modeling Ego-Motion Uncertainty for Moving Object Detection from a Mobile Platform. 2014 IEEE Intelligent Vehicles Symposium, Jun 2014, Dearborn, United States. pp.1332-1338. ⟨hal-01010997⟩
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