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Unsupervised vehicle re-identification using triplet networks

Abstract: Vehicle re-identification plays a major role in modern smart surveillance systems. Specifically, the task requires the capability to predict the identity of a given vehicle, given a dataset of known associations, collected from different views and surveillance cameras. Generally, it can be cast as a ranking problem: given a probe image of a vehicle, the model needs to rank all database images based on their similarities w.r.t the probe image. In line with recent research, we devise a metric learning model that employs a supervision based on local constraints. In particular, we leverage pairwise and triplet constraints for training a network capable of assigning a high degree of similarity to samples sharing the same identity, while keeping different identities distant in feature space. Eventually, we show how vehicle tracking can be exploited to automatically generate a weakly labelled dataset that can be used to train the deep network for the task of vehicle re-identification. Learning and evaluation is carried out on the NVIDIA AI city challenge videos.


Marin-Reyes, P. A.; Bergamini, L.; Lorenzo-Navarro, J.; Palazzi, A.; Calderara, S.; Cucchiara, R. "Unsupervised vehicle re-identification using triplet networks" IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2018-, usa, pp. 166 -171 , 2018, 2018 DOI: 10.1109/CVPRW.2018.00030

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