Unifying person and vehicle re-identification

Organisciak, D., Sakkos, D., Ho, E. S.L. , Aslam, N. and Shum, H. P. H. (2020) Unifying person and vehicle re-identification. IEEE Access, 8, pp. 115673-115684. (doi: 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: Articles
• Additional Information: This work was supported in part by the Royal Society under Grant IES\R2\181024 and Grant IES\R1\191147, and in part by the Defence and Security Accelerator under Grant DSTLX-100014072
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ho, Dr Edmond S. L
• Authors: Organisciak, D., Sakkos, D., Ho, E. S.L., Aslam, N., and Shum, H. P. H.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: IEEE Access
• Publisher: IEEE
• ISSN: 2169-3536
• ISSN (Online): 2169-3536
• Copyright Holders: Copyright © 2020 The Authors
• First Published: First published in IEEE Access 8: 115673 - 115684
• Publisher Policy: Reproduced under a Creative Commons licence