In the human brain, independent components of optical flows from the medial superior temporal area are speculated for motion cognition. Inspired by this hypothesis, a novel approach combining independent component analysis (ICA) with principal component analysis (PCA) is proposed in this paper for multiple moving objects detection in complex scenes—a major real-time challenge as bad weather or dynamic background can seriously influence the results of motion detection. In the proposed approach, by taking advantage of ICA’s capability of separating the statistically independent features from signals, the ICA algorithm is initially employed to analyze the optical flows of consecutive visual image frames. As a result, the optical flows of background and foreground can be approximately separated. Since there are still many disturbances in the foreground optical flows in the complex scene, PCA is then applied to the optical flows of foreground components so that major optical flows corresponding to multiple moving objects can be enhanced effectively and the motions resulted from the changing background and small disturbances are relatively suppressed at the same time. Comparative experimental results with existing popular motion detection methods for challenging imaging sequences demonstrate that our proposed biologically inspired vision-based approach can extract multiple moving objects effectively in a complex scene.
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