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• W2894980559 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." @default.
• W2894980559 created "2018-10-12" @default.
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• W2894980559 date "2018-06-01" @default.
• W2894980559 modified "2023-09-26" @default.
• W2894980559 title "Unsupervised Vehicle Re-identification Using Triplet Networks" @default.
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• W2894980559 doi "https://doi.org/10.1109/cvprw.2018.00030" @default.
• W2894980559 hasPublicationYear "2018" @default.
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