Unifying Person and Vehicle Re-Identification

Daniel Organisciak, Dimitrios Sakkos, Edmond S. L. Ho, Nauman Aslam and Hubert P. H. Shum
IEEE Access, 2020

 Impact Factor: 3.9 Citation: 10#

Unifying Person and Vehicle Re-Identification
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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.





 author={Organisciak, Daniel and Sakkos, Dimitrios and Ho, Edmond S. L. and Aslam, Nauman and Shum, Hubert P. H.},
 journal={IEEE Access},
 title={Unifying Person and Vehicle Re-Identification},


AU  - Organisciak, Daniel
AU  - Sakkos, Dimitrios
AU  - Ho, Edmond S. L.
AU  - Aslam, Nauman
AU  - Shum, Hubert P. H.
T2  - IEEE Access
TI  - Unifying Person and Vehicle Re-Identification
PY  - 2020
VL  - 8
SP  - 115673
EP  - 115684
DO  - 10.1109/ACCESS.2020.3004092
SN  - 2169-3536
ER  - 

Plain Text

Daniel Organisciak, Dimitrios Sakkos, Edmond S. L. Ho, Nauman Aslam and Hubert P. H. Shum, "Unifying Person and Vehicle Re-Identification," IEEE Access, vol. 8, pp. 115673-115684, IEEE, 2020.

Supporting Grants

Northumbria University

Postgraduate Research Scholarship (Ref: ): £65,000, Principal Investigator ()
Received from Faculty of Engineering and Environment, Northumbria University, UK, 2018-2021
Project Page

Similar Research

Daniel Organisciak, Chirine Riachy, Nauman Aslam and Hubert P. H. Shum, "Triplet Loss with Channel Attention for Person Re-Identification", Journal of WSCG - Proceedings of the 2019 International Conferences in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), 2019
Daniel Organisciak, Matthew Poyser, Aishah Alsehaim, Shanfeng Hu, Brian K. S. Isaac-Medina, Toby P. Breckon and Hubert P. H. Shum, "UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-Identification in Video Imagery", Proceedings of the 2022 International Conference on Computer Vision Theory and Applications (VISAPP), 2022
Brian K. S. Isaac-Medina, Matthew Poyser, Daniel Organisciak, Chris G. Willcocks, Toby P. Breckon and Hubert P. H. Shum, "Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark", Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021



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