mlsanityData to 7 Oct 2026

Complete Solution for Vehicle Re-ID in Surround-view Camera System

arXiv 2212.04126 ↗First posted 8 Dec 2022cs.CVcited by 0

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Abstract (arXiv)

Vehicle re-identification (Re-ID) is a critical component of the autonomous driving perception system, and research in this area has accelerated in recent years. However, there is yet no perfect solution to the vehicle re-identification issue associated with the car's surround-view camera system. Our analysis identifies two significant issues in the aforementioned scenario: i) It is difficult to identify the same vehicle in many picture frames due to the unique construction of the fisheye camera. ii) The appearance of the same vehicle when seen via the surround vision system's several cameras is rather different. To overcome these issues, we suggest an integrative vehicle Re-ID solution method. On the one hand, we provide a technique for determining the consistency of the tracking box drift with respect to the target. On the other hand, we combine a Re-ID network based on the attention mechanism with spatial limitations to increase performance in situations involving multiple cameras. Finally, our approach combines state-of-the-art accuracy with real-time performance. We will soon make the source code and annotated fisheye dataset available.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 49 references that are in the map. First posted on arXiv 8 Dec 2022.

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27 of 49 references.

References · 27

  1. 2017In Defense of the Triplet Loss for Person Re-Identificationcited by 784
  2. 2016Fully-Convolutional Siamese Networks for Object Trackingcited by 566
  3. 2018SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networkscited by 445
  4. 2018DropBlock: A regularization method for convolutional networkscited by 261
  5. 2016ECO: Efficient Convolution Operators for Trackingcited by 239
  6. 2018Harmonious Attention Network for Person Re-Identificationcited by 211
  7. 2015Learning Multi-Domain Convolutional Neural Networks for Visual Trackingcited by 209
  8. 2018Distractor-aware Siamese Networks for Visual Object Trackingcited by 187
  9. 2016Learning to Track at 100 FPS with Deep Regression Networkscited by 89
  10. 2016Siamese Instance Search for Trackingcited by 60
  11. 2020Parsing-based View-aware Embedding Network for Vehicle Re-Identificationcited by 53
  12. 2018Dual Attention Matching Network for Context-Aware Feature Sequence based Person Re-Identificationcited by 50
15 more
  1. 2018Batch DropBlock Network for Person Re-identification and Beyondcited by 45
  2. 2019Vehicle Re-identification with Viewpoint-aware Metric Learningcited by 45
  3. 2020Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-identificationcited by 37
  4. 2018RAM: A Region-Aware Deep Model for Vehicle Re-Identificationcited by 30
  5. 2020Orientation-aware Vehicle Re-identification with Semantics-guided Part Attention Networkcited by 26
  6. 2017Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposalscited by 23
  7. 2019Vehicle Re-Identification: an Efficient Baseline Using Triplet Embeddingcited by 21
  8. 20183D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-identificationcited by 14
  9. 2020Attribute-guided Feature Learning Network for Vehicle Re-identificationcited by 14
  10. 2018Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patternscited by 12
  11. 2018Deep Group-shuffling Random Walk for Person Re-identificationcited by 12
  12. 2019Cross-Entropy Adversarial View Adaptation for Person Re-identificationcited by 12
  13. 2019Attributes Guided Feature Learning for Vehicle Re-identificationcited by 12
  14. 2015Person Re-identification with Correspondence Structure Learningcited by 6
  15. 2017What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identificationcited by 5

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