A joint optical flow and principal component analysis approach for motion detection

Optical flow and its extensions have been widely used in motion detection and computer vision. In this paper, we apply principle component analysis (PCA) to analyze optical flows for better motion detection performance. The joint optical flow and PCA approach can efficiently detect moving objects and suppress small turbulence. It is effective in both static and dynamic background. It is particularly useful for motion detection from outdoor videos with low quality and small moving objects. Preliminary results demonstrate that this approach outperforms other existing methods by extracting the moving objects more completely with lower false alarms.