https://github.com/pavankumar0426-arch/realtimeobjecttracking
Real-Time Object Detection & Multi-Person Tracking with Bidirectional People Counting
https://github.com/pavankumar0426-arch/realtimeobjecttracking
opencv python pytorch yolov8
Last synced: 3 months ago
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Real-Time Object Detection & Multi-Person Tracking with Bidirectional People Counting
- Host: GitHub
- URL: https://github.com/pavankumar0426-arch/realtimeobjecttracking
- Owner: Pavankumar0426-arch
- Created: 2025-09-27T18:21:36.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-09-27T20:00:59.000Z (9 months ago)
- Last Synced: 2025-09-27T20:42:38.318Z (9 months ago)
- Topics: opencv, python, pytorch, yolov8
- Language: Python
- Homepage:
- Size: 6.95 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🚀 Real-Time People Tracking & Counting System
---
## 📄 Abstract
This project implements a **Real-Time Object Detection and Multi-Object Tracking system** with **Bidirectional People Counting** using **YOLOv8** and **DeepSORT**. The system accurately detects people in live video streams, assigns unique IDs for multi-person tracking, and counts people crossing a virtual line in both directions. The system provides real-time visualization, stores annotated videos, and generates statistical graphs of people count over time.
**Applications:**
Smart surveillance, crowd management, retail analytics, public space monitoring, and research.
---
## **Description**
The **Real-Time Object Detection & Multi-Object Tracking with People Counting** system combines:
- **YOLOv8:** High-speed, accurate object detection.
- **DeepSORT:** Robust multi-object tracking with unique IDs.
- **Bidirectional Counting:** Counts people moving upward and downward across a virtual line.
- **Visualization & Output:** Displays bounding boxes, track IDs, live counts, saves annotated video, and generates people count graphs.
**Key Applications:**
- Crowd monitoring in public spaces
- Retail store customer flow analysis
- Smart surveillance and security systems
- Event management and public transportation monitoring
---
## ✨ Features
- ✅ Real-time object detection with **YOLOv8**
- ✅ Multi-object tracking with **DeepSORT** and unique IDs
- ✅ Bidirectional people counting (up/down crossings)
- ✅ Live annotated video display with bounding boxes, IDs, and counts
- ✅ Save annotated video for analysis in `outputs/`
---
## 📁 Folder Structure
```
RealTimeObjectTracking/
├── src/
│ └── multi_track_count_bidirectional.py # Main script
├── models/
│ └── yolov8n.pt # YOLO model (auto-download works)
├── outputs/
<<<<<<< HEAD
│ └── people_count_bidirectional.mp4
├── requirements.txt
└── README.md
```
---
## ⚙️ Installation
1. **Create and activate a virtual environment**:
```
python -m venv objtrack_env
objtrack_env\Scripts\activate # Windows
# source objtrack_env/bin/activate # Linux/macOS
```
2. **Install dependencies**:
```
pip install -r requirements.txt
```
3. **▶️ Usage**
```
Install dependencies:
pip install -r requirements.txt
Navigate to the src/ folder:
cd src
Run the main script:
python multi_track_count_bidirectional.py
The webcam will open:
People will be tracked with bounding boxes and unique IDs.
The counting line will detect upward and downward crossings.
Counts will display live on the video.
Press q to stop the webcam.
The annotated video will be saved in:
outputs/people_count_bidirectional.mp4
🔧 Optional Customizations
Change the counting line position in the script (line_position).
Filter other object classes by COCO class ID (0 = person, 1 = bicycle, 2 = car, etc.).
Adjust YOLOv8 model (yolov8n.pt, yolov8s.pt, etc.) for speed vs accuracy.
Add trajectory lines for each tracked person.
```
---
## **🛠 Requirements:**
1. Python 3.8+
2. Webcam
3. GPU recommended for faster YOLOv8 inference
---
## **Dependencies**
1. lua
2. Copy code
3. ultralytics
4. opencv-python
5. opencv-python-headless
6. numpy
7.torch
8. torchvision
9. deep-sort-realtime
---
## **🔗 References**
1. YOLOv8 Documentation
2. DeepSORT Realtime Tracker
3. COCO Dataset Class IDs
---
## 👤 Author
### M. PAVAN KUMAR
Python & Computer Vision Enthusiast