Shadow Removal Based on HSV Characteristics

In vehicle detection systems, performing shadow removal from the extracted foreground based on HSV characteristics is crucial. By utilizing the HSV color space, we can identify and eliminate shadows within the foreground, thereby enhancing the accuracy and reliability of vehicle detection. This processing method leverages the inherent properties of the HSV color space: the Value (V) channel exhibits lower sensitivity to shadows, while the Hue (H) and Saturation (S) channels better preserve foreground object characteristics. The algorithm typically involves converting the input image from RGB to HSV color space, then analyzing pixel values across the three channels. Shadows are identified by detecting regions with significantly lower V-channel values while maintaining similar H and S characteristics as the background. Implementation often involves setting threshold values for the V channel and applying morphological operations to clean up the detected shadow regions. Through HSV image analysis, we perform shadow removal operations based on the low values in the brightness channel, resulting in cleaner foreground images. This approach enables more accurate vehicle detection while effectively eliminating interference caused by shadows in the detection results.