Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Edge-Based Rich Representation for Vehicle Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Robust vehicle and traffic information extraction for highway surveillance
EURASIP Journal on Applied Signal Processing
Adaptive background subtraction with multiple feedbacks for video surveillance
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
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A method to extract views of different orientations of a vehicle captured using multi cameras on roadside is proposed. We expect the use of multi views would increase classification performance in tasks such as identifying vehicle types/makes. This paper does not discuss classification work in details; it accepts the concept that with more data obtained through multi camera views, the use of distinctive orientations only would improve classifier's performance. Prior to this, we have to resolve practical issues such as identifying condition of vehicle merges and shadow. We use correlated data from multi cameras to find the most optimized cut for a merge situation. We also propose a novel approach of removing vehicle's shadow using blob reconstruction technique. Views of different vehicle orientations (in our experiment, left, rear, right) are interpreted using a 3D graph fitting on images from multi cameras.