The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Large-scale vehicle re-identification in urban surveillance videos
Abstract
Vehicle, as a significant object class in urban surveillance, attracts massive focuses in computer vision field, such as detection, tracking, and classification. Among them, vehicle re-identification (Re-Id) is an important yet frontier topic, which not only faces the challenges of enormous intra-class and subtle inter-class differences of vehicles in multicameras, but also suffers from the complicated environments in urban surveillance scenarios. Besides, the existing vehicle related datasets all neglect the requirements of vehicle Re-Id: 1) massive vehicles captured in real-world traffic environment; and 2) applicable recurrence rate to give cross-camera vehicle search for vehicle Re-Id. To facilitate vehicle Re-Id research, we propose a large-scale benchmark dataset for vehicle Re-Id in the real-world urban surveillance scenario, named “VeRi”. It contains over 40,000 bounding boxes of 619 vehicles captured by 20 cameras in unconstrained traffic scene. Moreover, each vehicle is captured by 2~18 cameras in different viewpoints, illuminations, and resolutions to provide high recurrence rate for vehicle Re-Id. Finally, we evaluate six competitive vehicle Re-Id methods on VeRi and propose a baseline which combines the color, texture, and highlevel semantic information extracted by deep neural network.
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Cites
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Self-citationScalable Person Re-identification: A Benchmark
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Cited by
TransReID: Transformer-based Object Re-Identification
He, S., Luo, H., Wang, P., Wang, F., Li, H., & Jiang, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01474 · Cited by 1,244
A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance
Liu, X., Liu, W., Mei, T., & Ma, H. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_53 · Cited by 641
Self-citationChen, L., Ai, H., Zhuang, Z., & Shang, C. (2018) · 2018 IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2018.8486597 · Cited by 530
An Edge Traffic Flow Detection Scheme Based on Deep Learning in an Intelligent Transportation System
Chen, C., Liu, B., Wan, S., Qiao, P., & Pei, Q. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3025687 · Cited by 429
Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-Temporal Path Proposals
Shen, Y., Xiao, T., Li, H., Yi, S., & Wang, X. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.210 · Cited by 303
Metadata
- Authors
- Liu, X., Liu, W., Ma, H., & Fu, H.
- Venue
- 2016 IEEE International Conference on Multimedia and Expo (ICME)
- Publisher
- IEEE
- Published
- 2016-07
- Pages
- 1-6
- OpenAlex
- W2512434173
- Cited by
- 513
- References
- 22
- FWCI
- 11.51
- Type
- conference paper
- Language
- en
- Open access
- closed
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