Large-scale vehicle re-identification in urban surveillance videos

By Xinchen Liu, Wu Liu, Huadong Ma, Huiyuan Fu. IEEE International Conference on Multimedia and Expo, 2016

545 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/abc8638968909ab0fdfbf1049009082df554a49e

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Citations in the corpus

Learning an Evolutionary Embedding via Massive Knowledge Distillation (2020, methodology): “It is obvious that the student network under EEL outperforms state-of-the-art methods including DRDL (Liu et al. 2016) and C2F-Rank (Guo et al.”
Unsupervised Domain Adaptive Re-Identification: Theory and Practice (2018, methodology): “We use VeRi-776 [16] and part of PKU-VehicleID [15] for vehicle re-ID experiments 3 , the details are included in Table 1.”
A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance (2016, methodology): “Moreover, in [9], the vehicle Re-Id is in an image-to-image manner, which means using a query image to search for the target images as in person Re-Id [24].”
CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels (2022, methodology): “We evaluate our method on four person re-identification datasets, including MSMT17 (Wei et al. 2018), Market-1501 (Zheng et al. 2015), DukeMTMC-reID (Ristani et al. 2016), Occluded-Duke (Miao et al. 2019), and two vehicle ReID datasets, VeRi-776 (Liu et al. 2016b) and VehicleID (Liu et al. 2016a).”
Real-Time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification (2018, background): “The proposed tracking framework can be easily transfered for other categories, by learning the appearance representation from corresponding datasets, such as vehicle re-identification [24].”
The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification (2020, background): “Through extensive experimentation in section 4, we show the effectiveness of our approach on multiple challenging vehicle re-id benchmarks [43, 22, 9, 27, 24], obtaining state-of-the-art results.”
An Edge Traffic Flow Detection Scheme Based on Deep Learning in an Intelligent Transportation System (2021, methodology): “We trained the network on a vehicle reidentification dataset (VeRi [51], [52] dataset).”
PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance (2018, methodology): “In our previous work [15], we propose to use multi-level appearance feature as the coarse filter to search for the vehicles that have similar appearance.”
Cluster Contrast for Unsupervised Person Re-Identification (2021, methodology): “Datasets We evaluate our proposed method on four largescale person re-identification (re-ID) benchmarks: Market1501 [53], DukeMTMC-reID [33], MSMT17 [44], PersonX [37], and one vehicle ReID dataset, VeRi-776 [27].”
TransReID: Transformer-based Object Re-Identification (2021, methodology): “We evaluate our proposed method on four person ReID datasets, Market-1501 [45], DukeMTMC-reID [29], MSMT17 [38], Occluded-Duke [26], and two vehicle ReID datasets, VeRi-776 [21] and VehicleID [20].”
Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-Grained Image Recognition (2019, background): “, classifying bird species [1, 35] and car models [14, 21, 37]), focuses on distinguishing subtle visual differences within a basic-level category.”
Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-Temporal Path Proposals (2017, methodology): “We compare our proposed approach with the state-ofthe-art methods [27, 28] and design several baselines on the VeRi-776 dataset to evaluate the effectiveness of our proposed method.”