Unifying Person and Vehicle Re-Identification
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 eff...
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doaj-e58161c343b644e8a2fe19214e2c987f2021-03-30T02:27:49ZengIEEEIEEE Access2169-35362020-01-01811567311568410.1109/ACCESS.2020.30040929121997Unifying Person and Vehicle Re-IdentificationDaniel Organisciak0Dimitrios Sakkos1Edmond S. L. Ho2https://orcid.org/0000-0001-5862-106XNauman Aslam3https://orcid.org/0000-0002-9500-3970Hubert P. H. Shum4https://orcid.org/0000-0001-5651-6039Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, U.K.Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, U.K.Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, U.K.Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, U.K.Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, U.K.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.https://ieeexplore.ieee.org/document/9121997/Person re-identificationvehicle re-identificationdeep learningtriplet loss |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Daniel Organisciak Dimitrios Sakkos Edmond S. L. Ho Nauman Aslam Hubert P. H. Shum |
spellingShingle |
Daniel Organisciak Dimitrios Sakkos Edmond S. L. Ho Nauman Aslam Hubert P. H. Shum Unifying Person and Vehicle Re-Identification IEEE Access Person re-identification vehicle re-identification deep learning triplet loss |
author_facet |
Daniel Organisciak Dimitrios Sakkos Edmond S. L. Ho Nauman Aslam Hubert P. H. Shum |
author_sort |
Daniel Organisciak |
title |
Unifying Person and Vehicle Re-Identification |
title_short |
Unifying Person and Vehicle Re-Identification |
title_full |
Unifying Person and Vehicle Re-Identification |
title_fullStr |
Unifying Person and Vehicle Re-Identification |
title_full_unstemmed |
Unifying Person and Vehicle Re-Identification |
title_sort |
unifying person and vehicle re-identification |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
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. |
topic |
Person re-identification vehicle re-identification deep learning triplet loss |
url |
https://ieeexplore.ieee.org/document/9121997/ |
work_keys_str_mv |
AT danielorganisciak unifyingpersonandvehiclereidentification AT dimitriossakkos unifyingpersonandvehiclereidentification AT edmondslho unifyingpersonandvehiclereidentification AT naumanaslam unifyingpersonandvehiclereidentification AT hubertphshum unifyingpersonandvehiclereidentification |
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