Motion Compensation Based Fast Moving Object Detection in Dynamic Background

This paper investigates robust and fast moving object detection in dynamic background. A motion compensation based approach is proposed to maintain an online background model, then the moving objects are detected in a fast fashion. Specifically, the pixel-level background model is built for each pixel, and is represented by a set of pixel values drawn from its location and neighborhoods. Given the background models of previous frame, the edge-preserving optical flow algorithm is employed to estimate the motion of each pixel, followed by propagating their background models to the current frame. Each pixel can be classified as foreground or background pixel according to the compensated background model. Moreover, the compensated background model is updated online by a fast random algorithm to adapt the variation of background. Extensive experiments on collected challenging videos suggest that our method outperforms other state-of-the-art methods, and achieves 8 fps in efficiency.

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