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Interactive web-based dashboard to manage traffic flow using YOLOX, DeepSORT

License: MIT License

Python 88.77% C++ 5.65% Jupyter Notebook 5.59%
dash deep-sort flask python pytorch yolox ngrok

traffic-flow-analysis's Introduction

Traffic-Management-System

Python Pytorch YOLO Tracking Dashboard Frontend Maintenance

Web Application

Built a web application using Flask and Dash.

demo

Installation

  • Clone this repository and check the requirements.txt:

    git clone https://github.com/sonnguyen129/Trafic-Management-System
    cd Trafic-Management-System
    pip install -r requirements.txt
  • Download YOLOX-S,M,L weights in this repository and add in ./weights folder and prepare your video input (Ex: this video)

  • Simply run Deploy webapp notebook.

    Note: The reason I don't deploy this repository to Heroku because it's very heavy, runs 1-2 FPS with CPU. So I used ngrok to be able to deploy online and used Colab to take advantage of the GPU. However, run it if you have a powerful enough GPU (Colab's GPU runs around 8-14 FPS, quite slow but better than CPU)

Problem Statement

Detecting and tracking technologies have been very popular in recent years. Suppose the authorities install CCTV cameras to control the flow and speed of traffic on sections of roads, intersections, etc. Thereby, it is possible to see the speed, direction, and type of vehicles on the road.

Why YOLOX

YOLOX is the state-of-the-art detector, known as YOLOv5 with some improvements. YOLOv5 is still anchor based detector and used hand-crafted assigning rules for training. Anchor free detector, advanced label assignment strategies and end-to-end detectors are integrated into YOLOX. In this project, I used COCO weights instead of training from scratch.

image

DeepSORT

DeepSORT was developed immediately SORT to address the omission problems associated with a high number of ID switches. The solution proposed by DeepSORT is based on the use of deep learning to extract features of objects to increase accuracy in the data association process. In addition, a linking strategy was also built called Matching Cascade to help link objects after disappearing for a while more effectively.

image

References

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traffic-flow-analysis's Issues

run with GPU

Hi,

Im only getting 1 fps with rtsp feed and its running with cpu.Any way to run dashboard.py with GPU?

thank you.

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