Optical flow and its extensions have been widely used in motion detection and computer vision. In the study, principal component analysis (PCA) is applied 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. Experimental results demonstrate that this approach outperforms other existing methods by extracting the moving objects more completely with lower false alarms. Saving strategies are developed to reduce computational complexity of optical flow calculation and PCA. Graphic processing unit (GPU)-based parallel implementation is developed, which shows excellent speed up performance.
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