Deep Learning for Vehicle Tracking and Wheel Detection

版本 1.1.2 (81.1 MB) 作者: Choi Youngsoo
Deep learning-based vehicle tracking and segmentation using SiamFC, DeepLabV3+, and Mask R-CNN for speed analysis.
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更新时间 2024/12/20

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The Vehicle Speed Analysis System is designed to automatically analyze the speed of vehicles in video footage. The project includes two main modules:
  1. Module 1 - Object Tracking:This module uses Siamese Fully Convolutional Networks (SiamFC) to track a target vehicle across frames. The system is enhanced with a Kalman filter to improve tracking accuracy under varying conditions such as occlusions, low resolution, and shape changes. The implementation uses MATLAB's Deep Learning Toolbox to integrate pre-trained models for efficient tracking.
  2. Module 2 - Instance Segmentation:This module focuses on precise segmentation of vehicle parts using DeepLabV3+ for semantic segmentation and Mask R-CNN for instance segmentation. The methodology is tailored to overcome challenges like scale variations and low resolution. A custom dataset, annotated for segmentation tasks, is used for model training and validation.
Key project highlights include:
  • Deployment of deep learning models with MATLAB for tracking and segmentation.
  • Comparative analysis of DeepLabV3+ and Mask R-CNN performance.
  • Pretrained models for Module 2 can be downloaded from the following links:
- Download Pretrained Model 1(MaskRCNN)
- Download Pretrained Model 2(DeepLabv3)
This system demonstrates the potential of MATLAB's deep learning frameworks to solve complex computer vision tasks effectively.

引用格式

Choi Youngsoo (2025). Deep Learning for Vehicle Tracking and Wheel Detection (https://www.mathworks.com/matlabcentral/fileexchange/176278-deep-learning-for-vehicle-tracking-and-wheel-detection), MATLAB Central File Exchange. 检索时间: .

https://www.matlabexpo.com/kr/2023/proceedings.html

MATLAB 版本兼容性
创建方式 R2024b
兼容 R2022a 到 R2024b 的版本
平台兼容性
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Module1

Module2

Module2/dataset_MaskRNN/New_labels

Module2/dataset_MaskRNN/imgs

版本 已发布 发行说明
1.1.2

Modifying image files
1.1.1

Modifying Mask-RCNN dataset

1.0.1

Image update
1.0.0