Detecting buildings in aerial images
Computer Vision, Graphics, and Image Processing
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Tracking Moving Objects as Spatio-Temporal Boundary Detection
SSIAI '02 Proceedings of the Fifth IEEE Southwest Symposium on Image Analysis and Interpretation
Moving Cast Shadow Elimination for Robust Vehicle Extraction Based on 2D Joint Vehicle/Shadow Models
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Vehicle Categorization: Parts for Speed and Accuracy
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Content-based event retrieval using semantic scene interpretation for automated traffic surveillance
IEEE Transactions on Intelligent Transportation Systems
Computer vision algorithms for intersection monitoring
IEEE Transactions on Intelligent Transportation Systems
Classifying color edges in video into shadow-geometry, highlight, or material transitions
IEEE Transactions on Multimedia
Simultaneous motion estimation and segmentation
IEEE Transactions on Image Processing
Dense estimation and object-based segmentation of the optical flow with robust techniques
IEEE Transactions on Image Processing
Feature-based tracking approach for detection of moving vehicle in traffic videos
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
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Intelligent traffic surveillance systems are assuming an increasingly important role in highway monitoring and city road management systems. Recently a novel feature was proposed to improve the accuracy of object localization and occlusion handling. It was constructed on the basis of the strong shadow under the vehicle in real-world traffic scene. In this paper, we use some statistical parameters of each frame to detect and segment these shadows. To demonstrate robustness and accuracy of our proposed approach, impressive results of our method in real traffic images including high congestion, noise, clutter, snow, and rain containing cast shadows, bad illumination conditions and occlusions, taken from both outdoor highways and city roads are presented.