This project tracks people using the YOLO and ByteTrack algorithms, counting individuals passing a marker and categorizing their direction as right-to-left or left-to-right.

Our crossing line is between the pole and the noticeboard bottom.

• Description
• Working
• Installation
• Usage
• Features
• Limitation
• Bibliography

This project utilizes the YOLO (You Only Look Once) object detection algorithm combined with the ByteTrack multi-object tracking algorithm to monitor and count people passing a specified marker. The direction of movement (right-to-left or left-to-right) is recorded, and counters are incremented accordingly.

1. Read Video:

   • The code starts by loading a video or connecting to a live camera feed. This is like pressing play on a video player.

2. Finding People (YOLO):

   • The code uses a deep learning model called YOLO (You Only Look Once) to find people in each frame of the video. Think of YOLO as a really smart pair of glasses that can spot people instantly in any picture.

3. Tracking People (ByteTrack):

   • Once YOLO spots a person, ByteTrack takes over to follow that person as they move from one frame to the next. ByteTrack is like a high-tech tracking algorithm that keeps an eye on each person so it knows where they go.

4. Counting People:

   • The code has a "marker" or an imaginary line in the video (that it gets from the mask.png file). Whenever a person crosses this line, the code notes down which direction they are moving:
     • If they cross from right to left, one counter goes up.
     • If they cross from left to right, another counter goes up.

5. Displaying Results:

   • The code continuously updates and shows the counts for how many people have crossed the line in each direction. This is like a scoreboard that keeps track of the movement of people in real time.

1. Clone the repository:

   git clone https://github.com/rushidarge/People-Tracking-and-Counting.git
   cd People-Tracking-and-Counting

2. Create and activate a virtual environment (optional but recommended):

   python3 -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

1. Prepare your input video or camera feed.
2. Update your path for video, mask, and out in config.json
3. Run the main tracking script:

   python get_count_dynamic.py

4. View the results and counters in the console output or as specified in the configuration.
5. Your video is saved in videos/predicition_output/

• Person detection using YOLO
• Multi-object tracking with ByteTrack
• Direction-based counting
• Easy configuration and customization

• We need to tune our logic sometimes we miss a person from counting.
• If people are overlapping we lose track of them.
• Two people walking simultaneously then we miss that person in counting, we need to place the camera strategically.
• To make it real-time we need GPU.

Yolo Model : https://github.com/WongKinYiu/yolov9

Bytetrack Algorithm: https://medium.com/tech-blogs-by-nest-digital/object-tracking-object-detection-tracking-using-bytetrack-0aafe924d292

Ultralytics: https://docs.ultralytics.com/modes/track/