Improving moving cast shadows suppression in traffic image sequences

Abstract

In recent years, video-based Intelligent Transportation Systems (ITS) have been of major importance for enforcing traffic management policies. Detection and tracking of moving vehicle is at the core of many applications dealing with traffic image sequences. For an accurate scene analysis in monocular image sequences, a robust segmentation of moving object from the static background is generally required. However, one of the main challenges in these applications is moving cast shadows, which often interfere with fundamental tasks such as object extraction and description. For this reason, shadow segmentation is an important step in image analysis. We propose a real-time and effective method for detecting vehicles from a sequence of traffic images taken by a single roadside mounted camera. The proposed algorithm includes three stages: first, extract moving object region and background region from the current input image, second, by adopting the various characteristics of shadow in luminance, chrominance, and gradient density, segment moving cast shadow region which is often caused by moving vehicle and, at last, Sobel edge detector is employed to detect edge pixels of the moving cast shadow in order to suppress all shadow pixels in the detected region. The proposed method has been tested on a number of typical monocular traffic-image sequences and the experimental results on the real-world videos show that the algorithm can effectively detect the associated moving cast shadow from the interested object.