Automatic Shadow Removal by Illuminance in HSV Color Space

In intelligent video surveillance systems, the detected moving objects often contain shadows which may deteriorate the performance of object detections. Therefore, shadow detection and removal is an important step employed after foreground extraction. Since HSV color space gives a better separation of chromaticity and intensity, it has been commonly adopted to detect and remove shadow. However, almost all the HSV color space based methods use static thresholds to separate shadows from foreground. In this paper, a dynamic threshold based method is proposed. In the proposed approach, the threshold prediction model is first established by a statistical analysis tool and then the predicted dynamic thresholds are used for shadow detection. Experiments on a self-built dataset show that the proposed method can get better reliability and robustness than the traditional methods using static thresholds.