Abstract
Vehicle re-identification (Re-ID) is a search for the similar vehicles in a multi-camera network usually having non-overlapping field-of-views. Supervised approaches have been used mostly for re-ID problem but they have certain limitations when it comes to real life scenarios. To cope with these limitations unsupervised learning techniques can be used. Unsupervised techniques have been successfully applied in the field of person re-identification. Having this in mind, this paper presents an unsupervised approach to solve the vehicle re-ID problem by training a base network architecture with a self-paced progressive unsupervised learning architecture which has not been applied to solve the vehicle re-ID problem. The algorithm has been extensively analyzed over two large available benchmark datasets VeRi and VehicleID for vehicle re-ID with image-to-image and cross-camera search strategies and the approach achieved better performance in most of the standard evaluation metrics when compared with the existing state-of-the-art supervised approaches.
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Bashir, R.M.S., Shahzad, M., Fraz, M.M. (2018). DUPL-VR: Deep Unsupervised Progressive Learning for Vehicle Re-Identification. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2018. Lecture Notes in Computer Science(), vol 11241. Springer, Cham. https://doi.org/10.1007/978-3-030-03801-4_26
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