Enhancing Vehicle Re-identification and Matching for Weaving Analysis
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Abstract (arXiv)
Vehicle weaving on highways contributes to traffic congestion, raises safety issues, and underscores the need for sophisticated traffic management systems. Current tools are inadequate in offering precise and comprehensive data on lane-specific weaving patterns. This paper introduces an innovative method for collecting non-overlapping video data in weaving zones, enabling the generation of quantitative insights into lane-specific weaving behaviors. Our experimental results confirm the efficacy of this approach, delivering critical data that can assist transportation authorities in enhancing traffic control and roadway infrastructure.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 27 references that are in the map. First posted on arXiv 5 Jul 2024.
Builds on
11 of 27 references.
References · 11
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2015SSD: Single Shot MultiBox Detectorcited by 3,130
- 2021TransReID: Transformer-based Object Re-Identificationcited by 435
- 2019CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identificationcited by 119
- 2019Simulating Content Consistent Vehicle Datasets with Attribute Descentcited by 36
- 2021Heterogeneous Relational Complement for Vehicle Re-identificationcited by 25
- 2020StRDAN: Synthetic-to-Real Domain Adaptation Network for Vehicle Re-Identificationcited by 14
- 2023So you think you can track?cited by 5
- 2024Real-time Lane-wise Traffic Monitoring in Optimal ROIscited by 4
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Citing papers per year: 2025: 1.
1 paper.