Real-time vehicle shadow detection

In many traffic-related applications, such as traffic management and structural health monitoring for roads, an accurate estimation of a moving vehicle's size and shape is needed before proceeding further. However, due to the presence of cast shadows, these properties cannot be obtained accurately using common object detection systems. To deal with the problem of misclassifying shadows as foreground, various methods have been introduced. Most of these methods often fail to distinguish shadow points from the foreground object when the boundary between the umbra and the object is unclear due to camouflage. A novel method for detecting moving shadows of vehicles in real-time applications is presented. The method is based on two measurements, namely, the illumination direction and the intensity measurements in the neighbouring pixels in a scanned line. A major advantage of using image lines for classification is the ability to solve the problem associated with camouflages. Experimental results show that the proposed method is efficient in real-time performances and has achieved higher detection rate and discrimination rate when compared with two well-known methods.