Realistic 3D simulation of shapes and shadows for image processing
CVGIP: Graphical Models and Image Processing
Model-based object tracking in monocular image sequences of road traffic scenes
International Journal of Computer Vision
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cast shadow segmentation using invariant color features
Computer Vision and Image Understanding
Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
Cast shadow detection in video segmentation
Pattern Recognition Letters
Shadow detection: A survey and comparative evaluation of recent methods
Pattern Recognition
A novel moving cast shadow detection of vehicles in traffic scene
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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In many image analysis and interpretation applications, shadows interfere with fundamental tasks such as object extraction and description. According to illumination, shadows interfere with moving vehicle extraction and location and recognition. For this reason, shadow segmentation is an important step in real-time vehicle recognition system. In this paper, we propose a simple and effective method for detection of moving cast shadows on a traffic surveillance scene. The proposed method exploits spectral and geometrical properties of shadows and relationship between the point in shadow region and space position and vehicle shape. Firstly, the cast shadows can be rough detected by spectral properties, and then feature points of occluding function are detected using wave transform, finally, the boundary between self-shadow and cast shadow is detected. The proposed method does not know in advance the light source direction and the color information of vehicle and background texture information. Our experimental results demonstrate that the proposed cast shadows segmentation method can detect the shadows regions accurately and completely. This is the foundation for future vehicle recognition and understanding.