Abstract— Vehicle re-identification is still a problem do not receive much attention in the multimedia and vision communities. Since most existing approaches mainly focus on the overall vehicle appearance for re-identification and do not consider the visual appearance changes of sides of vehicle, called local deformation. In this paper, we propose a vehicle re-identification method based on the authenticity of orthographic projection, in which three sides of vehicle are extracted, and the local deformation is explicitly minimized by scaling each pair of corresponding side to uniform size before computing similarity. To compute the similarity between two vehicle images, we 1) construct 3D bounding boxes around the vehicles, 2) extract sub-images of the three sides of each vehicle like a three-view drawing, 3) compute the similarity between each pair of corresponding side sub-images, and 4) use their weighted mean as the final measure of similarity. After computing the similarity between the query vehicle and all candidate vehicles, we rank these similarities and take the vehicle with the maximum similarity as the best match. To evaluate this approach, we use a dataset with 240 pairs of vehicle images extracted from surveillance videos shot at seven locations in different directions. The experimental results show that our proposed method can achieve 75.83% matching accuracy for the top-1 ranked vehicle and 91.25% accuracy for the top-5 vehicles.
Keywords— 3D bounding boxes, local deformation, vehicle re-identification, weighted mean.
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