https://github.com/utkarsh251106/tracking-with-yolo

This project uses YOLOv8 and DeepSORT to detect and track children and adults in video streams. It assigns unique IDs, handles re-tracking after occlusions, and outputs an annotated video with labeled bounding boxes.
https://github.com/utkarsh251106/tracking-with-yolo

computer-vision deep-learning deepsort machine-learning numpy opencv python torch ultralytics yolov8

Last synced: 9 days ago
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This project uses YOLOv8 and DeepSORT to detect and track children and adults in video streams. It assigns unique IDs, handles re-tracking after occlusions, and outputs an annotated video with labeled bounding boxes.

• Host: GitHub
• URL: https://github.com/utkarsh251106/tracking-with-yolo
• Owner: Utkarsh251106
• Created: 2024-09-08T17:30:23.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2025-01-18T15:30:00.000Z (about 1 month ago)
• Last Synced: 2025-01-18T16:31:46.328Z (about 1 month ago)
• Topics: computer-vision, deep-learning, deepsort, machine-learning, numpy, opencv, python, torch, ultralytics, yolov8
• Language: Jupyter Notebook
• Homepage:
• Size: 13.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Description

Tracking with YOLO is an object detection and tracking project designed to track and count the number of children and adults in a video. The dataset was annotated using Roboflow. For the actual execution of the program, the OpenCV library is used to handle camera and image-related tasks, NumPy is used for various mathematical calculations, and Ultralytics' YOLOv8 is used for object detection. Finally, the Deep SORT algorithm is used for tracking and occlusion prevention purposes. 

# How to run it?

### Step 1: Clone the Repository:

```bash

git clone https://github.com/Utkarsh251106/Tracking-with-YOLO

```

### Step 2: Create a conda environment:

```bash

conda create -n venv python=3.11.9 -y

conda activate venv

```

### Step 3: Install the requirements:

```bash

pip install -r requirements.txt

```

### Step 4: To run the code:

```bash

# Start the Jupyter Notebook environment using the command

jupyter notebook

```

### Step 5: Run your Code.ipynb file