Moving Object Detection With a Freely Moving Camera via Background Motion Subtraction

Abstract

Detection of moving objects in a video captured by a freely moving camera is a challenging problem in computer vision. Most existing methods often assume that the background (BG) can be approximated by dominant single plane/multiple planes or impose significant geometric constraints on BG, or utilize a complex BG/foreground probabilistic model. Instead, we propose a computationally efficient algorithm that is able to detect moving objects accurately and robustly in a general 3D scene. This problem is formulated as a coarse-to-fine thresholding scheme on the particle trajectories in the video sequence. First, a coarse foreground (CFG) region is extracted by performing reduced singular value decomposition on multiple matrices that are built from bundles of particle trajectories. Next, the BG motion of pixels in the CFG region is reconstructed by a fast inpainting method. After subtracting the BG motion, the fine foreground is segmented out by an adaptive thresholding method that is capable of solving multiple-moving-objects scenarios. Finally, the detected foreground is further refined by the mean-shift segmentation method. Extensive simulations and a comparison with the state-of-the-art methods verify the effectiveness of the proposed method.