Shadow Removal in Outdoor Video Sequences by Automatic Thresholding of Division Images
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Shadows Removal by Edges Matching
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Edge-Based Algorithm for Shadows and Ghosts Removing
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Adaptive shadow estimator for removing shadow of moving object
Computer Vision and Image Understanding
An efficient and robust moving shadow removal algorithm and its applications in ITS
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Shadow detection: A survey and comparative evaluation of recent methods
Pattern Recognition
A survey of cast shadow detection algorithms
Pattern Recognition Letters
Monocular vision-based target detection on dynamic transport infrastructures
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
Cast shadow detection based on semi-supervised learning
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
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We propose a novel adaptive technique for detecting moving shadows and distinguishing them from moving objects in video sequences. Most methods for detecting shadows work in a static setting with significant human input. To remove these limitations, we propose a more general semi-supervised learning technique to tackle the problem. First, we exploit characteristic differences in color and edges in the video frames to come up with a set of features useful for classification. Second, we use a learning technique that employs Support Vector Machines and the Co-training algorithm, that relies on a small set of human-labeled data. We observe a surprising phenomenon that Co-training can counter the effects of changing underlying probability distributions in the feature space. From the standpoint of detecting shadows, once deployed, the proposed method can dynamically adapt to varying conditions without any manual intervention, and performs better classification than previous methods on static and dynamic environments alike. The strengths of the proposed technique are the small quantity of human labeled data required, and the ability to adapt automatically to changing scene conditions.