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A Strong Baseline for Vehicle Re-Identification [article]

Su V. Huynh, Nam H. Nguyen, Ngoc T. Nguyen, Vinh TQ. Nguyen, Chau Huynh, Chuong Nguyen
2021 arXiv   pre-print
Vehicle Re-Identification (Re-ID) aims to identify the same vehicle across different cameras, hence plays an important role in modern traffic management systems.  ...  In this paper, we first analyze the main factors hindering the Vehicle Re-ID performance.  ...  Conclusion In this paper, we proposed a strong baseline for the vehicle re-identification problem.  ... 
arXiv:2104.10850v1 fatcat:gzfj2om445dctn3usideqwusly

A Strong and Efficient Baseline for Vehicle Re-Identification Using Deep Triplet Embedding

Ratnesh Kumar, Edwin Weill, Farzin Aghdasi, Parthasarathy Sriram
2020 Journal of Artificial Intelligence and Soft Computing Research  
In this paper we tackle the problem of vehicle re-identification in a camera network utilizing triplet embeddings.  ...  outperform most of the existing state-of-the-art approaches for vehicle re-identification.  ...  Conclusion and Future Work In this paper we propose a strong baseline for vehicle re-identification using the best practices in learning triplet embedding [12] .  ... 
doi:10.2478/jaiscr-2020-0003 fatcat:hwtyl4uxrfgahp6jqxfdgyjijm

The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification [article]

Pirazh Khorramshahi, Neehar Peri, Jun-cheng Chen, Rama Chellappa
2020 arXiv   pre-print
In this paper, we present Self-supervised Attention for Vehicle Re-identification (SAVER), a novel approach to effectively learn vehicle-specific discriminative features.  ...  In recent years, the research community has approached the problem of vehicle re-identification (re-id) with attention-based models, specifically focusing on regions of a vehicle containing discriminative  ...  To train our deep feature extraction module, we use techniques proposed in "Bag of Tricks" [28] and adapt them for vehicle re-identification, offering a strong baseline.  ... 
arXiv:2004.06271v3 fatcat:z3cn4maofbhshg35s2i3cz42o4


V. V. Kniaz, P. Moshkantseva
2021 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
We developed the ThermalReID framework for cross-modality object re-identification. We evaluated our framework and two modern baselines on the task of object ReID for four object classes.  ...  Our framework aims to provide continuous object detection and re-identification while monitoring a region from a UAV.  ...  A specific algorithm for vehicle re-identification uses the rich annotation information available from large-scale dataset for vehicle re-identification (Wang et al., 2019) .  ... 
doi:10.5194/isprs-archives-xliv-2-w1-2021-131-2021 fatcat:hfw62uz7znalfjre35bw2fhife

Multi-Domain Learning and Identity Mining for Vehicle Re-Identification [article]

Shuting He, Hao Luo, Weihua Chen, Miao Zhang, Yuqi Zhang, Fan Wang, Hao Li, Wei Jiang
2020 arXiv   pre-print
This paper introduces our solution for the Track2 in AI City Challenge 2020 (AICITY20). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic data.  ...  Our solution is based on a strong baseline with bag of tricks (BoT-BS) proposed in person ReID.  ...  Our method is based on a strong baseline [13, 14] in person ReID. For vehicle ReID, Liu et al.  ... 
arXiv:2004.10547v2 fatcat:dvcljt7c7ratvpooe6nrmj3nle

Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification [article]

Yongming Rao, Guangyi Chen, Jiwen Lu, Jie Zhou
2021 arXiv   pre-print
re-identification.  ...  Empirically, we evaluate our method on a wide range of fine-grained recognition tasks where attention plays a crucial role, including fine-grained image categorization, person re-identification, and vehicle  ...  grant from the Beijing Academy of Artificial Intelligence (BAAI), and in part by a grant from the Institute for Guo Qiang, Tsinghua University.  ... 
arXiv:2108.08728v2 fatcat:5imvpqpxcjbbxj5fbcotmbmnjy

A Generated Multi Branch Feature Fusion Model for Vehicle Re-identification

Hu Zhijun, Raja Soosaimarian Peter Raj, Sun Lilei, Wu Lian, Cheng Xianjing
2021 Brazilian Archives of Biology and Technology  
The experimental results show that our proposed method is much better than the state-of-the-art vehicle re-identification methods.  ...  Existing research uses the fusing features for fusion and we use the global vehicle features for fusion.  ...  CONCLUSION In this paper, we propose a method of generated multi branch feature fusion for vehicle re-identification named GMBFF.  ... 
doi:10.1590/1678-4324-2021210296 fatcat:p4ib7hs4qrhttgc73goy57tru4

Part-based Multi-stream Model for Vehicle Searching

Ya Sun, Minxian Li, Jianfeng Lu
2018 2018 24th International Conference on Pattern Recognition (ICPR)  
Secondly, a new Part-based Multi-Stream Model (PMSM) is designed and optimized for vehicle retrieval and re-identification tasks.  ...  Firstly, a method is proposed to estimate if a patch in a raw vehicle image is discriminative or not.  ...  CONCLUSION In this paper, a part-based multi-stream model (PMSM) is proposed for vehicle re-identification and vehicle retrieval tasks.  ... 
doi:10.1109/icpr.2018.8546191 dblp:conf/icpr/SunLL18 fatcat:r6berhqtnbdkfkor2tti3bgbbe

Multi-Spectral Vehicle Re-Identification: A Challenge

Hongchao Li, Chenglong Li, Xianpeng Zhu, Aihua Zheng, Bin Luo
Our work provides a benchmark dataset for RGB-NIR and RGB-NIR-TIR multi-spectral vehicle Re-ID and a baseline network for both research and industrial communities.  ...  Vehicle re-identification (Re-ID) is a crucial task in smart city and intelligent transportation, aiming to match vehicle images across non-overlapping surveillance camera views.  ...  Introduction Vehicle re-identification (Re-ID) is to identify vehicle images from the gallery that shares the same identity as the given probe.  ... 
doi:10.1609/aaai.v34i07.6796 fatcat:oqtfculpdrbp3fgslibpz6m7x4

Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposals [article]

Yantao Shen, Tong Xiao, Hongsheng Li, Shuai Yi, Xiaogang Wang
2017 arXiv   pre-print
Existing vehicle re-identification methods ignored or used over-simplified models for the spatio-temporal relations between vehicle images.  ...  However, unlike person re-identification, the visual differences between pairs of vehicle images are usually subtle and even challenging for humans to distinguish.  ...  The main contribution of our method is two-fold. (1) We propose a two-stage framework for vehicle re-identification.  ... 
arXiv:1708.03918v1 fatcat:3rfxnsfcezamzbgnio2z5y74pq

Unifying Person and Vehicle Re-identification

Daniel Organisciak, Dimitrios Sakkos, Edmond S. L. Ho, Nauman Aslam, Hubert P. H. Shum
2020 IEEE Access  
Person and vehicle re-identification (re-ID) are important challenges for the analysis of the burgeoning collection of urban surveillance videos.  ...  INDEX TERMS Person re-identification, vehicle re-identification, deep learning, triplet loss.  ...  We propose a unified framework for pedestrians and vehicles re-identification using a new unified data set, PVUD, which challenges re-ID systems to be capable of handling both tasks simultaneously.  ... 
doi:10.1109/access.2020.3004092 fatcat:6uiwivuqbbfwtd6ttf63ykskhe

A Multi-Feature Learning Model with Enhanced Local Attention for Vehicle Re-Identification

Wei Sun, Xuan Chen, Xiaorui Zhang, Guangzhao Dai, Pengshuai Chang, Xiaozheng He
2021 Computers Materials & Continua  
To solve this issue, a multi-feature learning model with enhanced local attention for vehicle re-identification (MFELA) is proposed in this paper. The model consists of global and local branches.  ...  It has gradually become a core technology of intelligent transportation system. Most existing vehicle re-identification models adopt the joint learning of global and local features.  ...  In this paper, we propose a multi-feature learning model with enhanced local attention for vehicle re-identification(MFELA).  ... 
doi:10.32604/cmc.2021.021627 fatcat:ftug3i2lbjhp3lckor3rjhwr2e

Multi‐label based view learning for vehicle re‐identification

Yichu Liu, Haifeng Hu, Dihu Chen
2021 Electronics Letters  
Due to the high similarity of different vehicles with similar appearances and the great diversity of camera viewpoints, vehicle re-identification (ReID) is still a challenging task.  ...  First, the vehicle orientation estimation module is responsible for detecting key-points of the vehicle and predicting its orientation, which provides the orientation label for the multi-label based learning  ...  ,Liu, B., et al.: Multi-labelbased similarity learning for vehicle re-identification.  ... 
doi:10.1049/ell2.12377 fatcat:xaza3qt6ivcwzlznrevuy73oju

Vehicle Re-Identification: an Efficient Baseline Using Triplet Embedding [article]

Ratnesh Kumar, Edwin Weill, Farzin Aghdasi, Parthsarathy Sriram
2019 arXiv   pre-print
In this paper we tackle the problem of vehicle re-identification in a camera network utilizing triplet embeddings.  ...  approaches proposed in the vehicle re-identification literature.  ...  Table 13 Conclusion and Future Work In this paper we propose a strong baseline for vehicle re-identification using the best practices in learning deep triplet embedding [11] .  ... 
arXiv:1901.01015v4 fatcat:5d4u4aykyzfg5gnjh3nhpygidm

An Empirical Study of Vehicle Re-Identification on the AI City Challenge [article]

Hao Luo, Weihua Chen, Xianzhe Xu, Jianyang Gu, Yuqi Zhang, Chong Liu, Yiqi Jiang, Shuting He, Fan Wang, Hao Li
2021 arXiv   pre-print
This paper introduces our solution for the Track2 in AI City Challenge 2021 (AICITY21). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic data.  ...  (UDA) training, post-processing, model ensembling in this challenge. (1) Both cropping training data and using synthetic data can help the model learn more discriminative features. (2) Since there is a  ...  [16, 17] proposed a strong baseline [16, 17] in person ReID, which also performers well in vehicle ReID. For vehicle ReID, Liu et al.  ... 
arXiv:2105.09701v1 fatcat:qh2v2raslvgebjp4vsf3w7wfvq
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