Unifying Person and Vehicle Re-Identification

Organisciak, Daniel, Sakkos, Dimitris, Ho, Edmond, Aslam, Nauman and Shum, Hubert (2020) Unifying Person and Vehicle Re-Identification. IEEE Access, 8. pp. 115673-115684. ISSN 2169-3536

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Official URL: https://doi.org/10.1109/ACCESS.2020.3004092

Abstract

Person and vehicle re-identification (re-ID) are important challenges for the analysis of the burgeoning collection of urban surveillance videos. To efficiently evaluate such videos, which are populated with both vehicles and pedestrians, it would be preferable to have one unified framework with effective performance across both domains. Unfortunately, due to the contrasting composition of humans and vehicles, no architecture has yet been established that can adequately perform both tasks. We release a Person and Vehicle Unified Data Set (PVUD) comprising of both pedestrians and vehicles from popular existing re-ID data sets, in order to better model the data that we would expect to find in the real world. We exploit the generalisation ability of metric learning to propose a re-ID framework that can learn to re-identify humans and vehicles simultaneously. We design our network, MidTriNet, to harness the power of mid-level features to develop better representations for the re-ID tasks. We help the system to handle mixed data by appending unification terms with additional hard negative and hard positive mining to MidTriNet. We attain comparable accuracy training on PVUD to training on the comprising data sets separately, supporting the system's generalisation power. To further demonstrate the effectiveness of our framework, we also obtain results better than, or competitive with, the state-of-the-art on each of the Market-1501, CUHK03, VehicleID and VeRi data sets.

Item Type: Article
Uncontrolled Keywords: Person re-identification, vehicle re-identification, deep learning, triplet loss
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 16 Oct 2020 12:22
Last Modified: 16 Oct 2020 12:30
URI: http://nrl.northumbria.ac.uk/id/eprint/44536

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