Vehicle-Trajectory-Prediction-in-Ho-Chi-Minh-city-streets

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #llm #golang #machine-learning #nodejs #java #awesome-list #pytorch #mcp #openai #open-source

Vehicle-Trajectory-Prediction-in-Ho-Chi-Minh-city-streets

(★ 16)

Predict the trajectory of the vehicles in HCM city streets with YOLOv7 + DeepSORT + CNN-LSTM/CNN-GRU.

Vehicle-Trajectory-Prediction-in-Ho-Chi-Minh-city-streets 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Vehicle Trajectory Prediction in Ho Chi Minh City streets.

This is the final project for DS201 - Deep Learning for Data Science of the University of Information Technology course.

The purpose of this project is to predict the trajectory of the vehicles in Thu Duc District, Ho Chi Minh city, Vietnam. There are 3 main sub-tasks for this project.
There are Vehicle Detection, Vehicle Tracking and Vehicle Trajectory Prediction.

I choose YOLOv7 for Vehicle Detection Task, DeepSORT for Vehicle Tracking Task and CNN-LSTM/CNN-GRU for Vehicle Trajectory Task.

### :file_folder: Vehicle Trajectory Training Dataset

You can access the dataset used for training the Vehicle Trajectory model by following this link: [Vehicle Trajectory Dataset on Kaggle](https://www.kaggle.com/datasets/tonchow/uit-vehpredtraj-dataset).

### :notebook: Training Model Notebook

To explore the training model and run experiments, you can use the following Google Colab notebook: [Vehicle Trajectory Training Model Notebook](https://colab.research.google.com/drive/1cmuh40m8CoNlMCn6_GFf8nw4bNr-Tz9O?usp=sharing
).

Feel free to use these resources to enhance your understanding of vehicle trajectory modeling and analysis.

### Tech Stack:
<p>
<img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/pytorch/pytorch-original.svg" height="50px" width="50px"/>
<img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/tensorflow/tensorflow-original.svg" height="50px" width="50px" />
<img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/python/python-original.svg" height="50px" width="50px" />
<img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/opencv/opencv-original.svg"  height="50px" width="50px"/>
<img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/vscode/vscode-original.svg" height="50px" width="50px"/>
<img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/anaconda/anaconda-original.svg" height="50px" width="50px"/>

</p>

### Demo

https://user-images.githubusercontent.com/79329526/215509399-ed339285-a138-4243-a7f9-5b04b788e0aa.mp4

You can see in the small demo. The center of every bounding boxes is the current state, the model is trying to predict the center point in the next state.

In this project, we use Time Series Approach to predict the trajectory of the vehicle.

### Set up
Download the pretrained weight of YoloV7 in this link: https://github.com/WongKinYiu/yolov7

Go to `IO_data/input/video` folder to store the download or recorded of the vehicle on the street.

Go to `run.py` to change some hyperparamater of function __tracker.track_video__

Create the virtual environment

```
conda env create -f environment.yml
```

Activate the virtual environment
```
conda activate vehtrajpred
```
Run the inference file
```
python3 run.py
```

Vehicle-Trajectory-Prediction-in-Ho-Chi-Minh-city-streets

Vehicle-Trajectory-Prediction-in-Ho-Chi-Minh-city-streets 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Vehicle-Trajectory-Prediction-in-Ho-Chi-Minh-city-streets

Vehicle-Trajectory-Prediction-in-Ho-Chi-Minh-city-streets 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README