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PakVehicle-ReID: a multi-perspective benchmark for vehicle re-identification in unconstrained urban road environment

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Abstract

The challenge of re-identifying vehicles in urban city surveillance systems and major traffic arteries such as highways and roads is an important area of research. The advent of large-scale benchmarks such as VeRI-776 and Vehicle-ID has propelled efforts to enhance search operations from large databases for re-identification. However, several unresolved challenges associated with vehicle re-identification in unconstrained environments remain to be explored. In order to foster research in this field, we have compiled a new multi-perspective dataset, PAKVehicle-ReId, captured by real-world surveillance cameras in urban cities of the developing country of Pakistan. To the best of our knowledge, this is the first such dataset collected under unconstrained conditions in a developing Asian region. The dataset comprises 80,000 images of 20,000 unique vehicles. Additionally, a deep learning-based technique for extracting multi-dimensional robust features for vehicle re-identification using convolutional neural networks has been proposed. The results show the effectiveness of the proposed method on the PAKVehicle-ReId dataset as well as on two other existing datasets, VeRI-776 and VehicleID. The code and link to the dataset can be obtained from the following GitHub repository: https://github.com/Vision-At-SEECS/PakvehicleReId.

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Data availibility statement

The dataset, source code and trained weights for the proposed architecture can be found at this link: https://bit.ly/3SJzHhL.

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Authors and Affiliations

  1. National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Hasan Ali Asghar, Bostan Khan, Zuhair Zafar & Muhammad Moazam Fraz

  2. Faculty of Computer Science, Universiti Malaya, Kuala Lumpur, Malaysia

    Aznul Qalid Md Sabri

Authors
  1. Hasan Ali Asghar
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  2. Bostan Khan
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  3. Zuhair Zafar
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  4. Aznul Qalid Md Sabri
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  5. Muhammad Moazam Fraz
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Correspondence to Muhammad Moazam Fraz.

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Asghar, H.A., Khan, B., Zafar, Z. et al. PakVehicle-ReID: a multi-perspective benchmark for vehicle re-identification in unconstrained urban road environment. Multimed Tools Appl 83, 53009–53024 (2024). https://doi.org/10.1007/s11042-023-17070-6

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  • DOI: https://doi.org/10.1007/s11042-023-17070-6

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