A channel awareness vehicle detector
IEEE Transactions on Intelligent Transportation Systems
Incremental unsupervised three-dimensional vehicle model learning from video
IEEE Transactions on Intelligent Transportation Systems
Vehicle headlights detection using markov random fields
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
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This paper presents a multilevel framework to detect and handle vehicle occlusion. The proposed framework consists of the intraframe, interframe, and tracking levels. On the intraframe level, occlusion is detected by evaluating the compactness ratio and interior distance ratio of vehicles, and the detected occlusion is handled by removing a ldquocutting regionrdquo of the occluded vehicles. On the interframe level, occlusion is detected by performing subtractive clustering on the motion vectors of vehicles, and the occluded vehicles are separated according to the binary classification of motion vectors. On the tracking level, occlusion layer images are adaptively constructed and maintained, and the detected vehicles are tracked in both the captured images and the occlusion layer images by performing a bidirectional occlusion reasoning algorithm. The proposed intraframe, interframe, and tracking levels are sequentially implemented in our framework. Experiments on various typical scenes exhibit the effectiveness of the proposed framework. Quantitative evaluation and comparison demonstrate that the proposed method outperforms state-of-the-art methods.