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A novel two-stage algorithm for non-parametric cast shadow recognition.
| Content Provider | CiteSeerX |
|---|---|
| Author | Roser, Martin Lenz, Philip |
| Abstract | Abstract — Environment perception and scene understanding is an important issue for modern driver assistance systems. However, adverse weather situations and disadvantageous illumination conditions like cast shadows have a negative effect on the proper operation of these systems. In this paper, we propose a novel approach for cast shadow recognition in monoscopic color images. In a first step, shadow edge candidates are extracted evaluating binarized channels in the color-opponent and perceptually uniform CIE L*a*b* space. False detections are rejected in a second verification step, using SVM classification and a combination of meaningful color features. We introduce a non-parametric representation for complex shadow edge geometries that enables utilizing shadow edge information for improving downstream vision-based driver assistance systems. A quantitative evaluation of the classification performance as well as results on multiple real-world traffic scenes show a reliable cast shadow recognition with only a few false detections. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Novel Two-stage Algorithm Non-parametric Cast Shadow Recognition False Detection Quantitative Evaluation Classification Performance Proper Operation Novel Approach Shadow Edge Candidate Svm Classification Abstract Environment Perception Disadvantageous Illumination Condition Reliable Cast Shadow Recognition Binarized Channel Non-parametric Representation Second Verification Step Multiple Real-world Traffic Adverse Weather Situation Uniform Cie Scene Understanding Cast Shadow Recognition Complex Shadow Edge Geometry Cast Shadow Monoscopic Color Image Negative Effect Important Issue Meaningful Color Feature Edge Information First Step Downstream Vision-based Driver Assistance System Modern Driver Assistance System |
| Content Type | Text |
