Technology Sharing

한어Русский языкEnglishFrançaisIndonesianSanskrit日本語DeutschPortuguêsΕλληνικάespañolItalianoSuomalainenLatina

Table of contents

1. Background of the topic and research significance

2. Current status of research at home and abroad

3. Algorithm Innovation

IV. Experiment and Results Analysis

5. System Implementation

VI. Summary and Outlook

---

This paper, titled "Research on Vehicle Re-ID Algorithm and System Implementation Based on Deep Learning", was written by Qi Tiantian from East China Normal University. It aims to study how to improve the accuracy of vehicle re-ID using deep learning technology and build a vehicle intelligent re-ID system based on this algorithm. The paper covers the background, research status, algorithm innovation and system implementation of vehicle re-ID technology.

1. Background of the topic and research significance

As the number of vehicles increases, traditional manual traffic control becomes increasingly difficult, and intelligent transportation systems have emerged. As an important part of intelligent transportation, vehicle re-identification technology can identify specific vehicles in images or videos from different perspectives, which is of great significance for tasks such as intelligent security and vehicle tracking. However, existing license plate recognition technology has problems such as license plate occlusion and fake license plates, making the research on vehicle re-identification without license plates particularly important.

2. Current status of research at home and abroad

The paper reviews vehicle re-identification methods based on global features, local features and attention mechanisms. The global feature method extracts the overall features of the vehicle for identification, but tends to ignore local details; the local feature method extracts local details of the vehicle to distinguish similar vehicles, but existing methods often ignore the spatial structural relationship between local features; the attention mechanism improves recognition accuracy by enhancing the model's attention to important features, but ignores the correlation between different feature channels.