Shadow Elimination in Traffic Video Segmentation

Shadow detection is critical for robust and reliable vision-based systems for traffic vision analysis. Shadow points are often misclassified as object points causing errors in localization, segmentation, tracking and classification of moving vehicles. This paper proposes a novel shadow elimination method SEBG for resolving shadow occlusion problems of vehicle analysis. Different from some traditional method which only consider intensity properties, this method introduces gradient feature to eliminate shadows. In this approach, moving foregrounds are first segmented from background by using a background subtraction technique. For all moving pixels, the approach SEBG using gradient feature to detect shadow pixels is presented in detail. This method is based on the observation that shadow regions present same textural characteristics in each frame of the video as in the corresponding adaptive background model. Gradient feature is robust to illumination changes. The method also needs no predefined parameters, which can well adapt to other video scene. Results validate the algorithm’s good performance on traffic video.