Shadow detection with multi-feature fusion and MMSE optimization

In order to reduce the interference of shadow to detection results of moving targets, a shadow detection method is proposed, which can be made as the post-processing step of motion detection methods. On the basis of the target list detected by motion detection methods, this method extracts the brightness, contrast and structural features of the pixels between adjacent frames for the three color channels R, G and B, and fuses the three types of features to generate the similarity measure. And the objective function is designed according to the MMSE(minimum mean square error) criterion. The optimal pixel segmentation thresholds are obtained by the optimal solution, to detect and eliminate the shadow pixels in the motion detection targets. The shadow detection experiments are carried out on the shadow subset of Changedetection.net dataset. The experimental results show that this method has high detection rate and low detection time.

Abstract licence: CC BY-NC

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