README

Person Detection & Tracking with YOLOv8 and DeepSORT

This project implements a person detection and tracking pipeline using the YOLOv8n object detection model and DeepSORT for multi-object tracking, along with OSNet for person re-identification. The code processes a video, detects persons in each frame, and tracks them across the video, ensuring that each person is uniquely identified even after temporary occlusion.

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Overview

The project aims to:

1. Detect persons in each frame using the YOLOv8n model.
2. Track the detected persons over time using DeepSORT.
3. Re-identify persons after occlusion or re-entry using the OSNet re-identification model.
4. Output the video with annotated bounding boxes and unique IDs for each person.

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Requirements

The following Python packages are required:

• numpy
• torch
• ultralytics (YOLOv8 model)
• deep-sort-realtime
• opencv-python
• torchreid (for OSNet re-identification)
• gdown

To install the required dependencies, run:

pip install -r requirements.txt

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Setup

1. Download YOLOv8n Pretrained Model: The pretrained YOLOv8n model is automatically downloaded by the ultralytics package. Make sure you are connected to the internet for this step.

2. Download OSNet Pretrained Model: The OSNet model is built into the torchreid package and is loaded directly in the script.

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Code Structure

• yolo_model = YOLO("yolov8n.pt"): Loads the YOLOv8n model, which is used to detect objects (particularly persons) in the video frames.

• DeepSORT Initialization: The DeepSORT tracker is initialized to handle object tracking by associating bounding boxes across frames.

• OSNet for Person Re-identification: OSNet is used to generate embeddings for each detected person. This allows the model to re-identify people after occlusions.

• Main Loop: The code reads the video frame-by-frame, applies YOLO to detect persons, uses DeepSORT for tracking, and applies re-identification with OSNet to ensure consistent IDs throughout the video.

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Explanation of Key Components

1. YOLOv8 Model for Person Detection

• YOLOv8n is a state-of-the-art object detection model designed to detect multiple classes, including persons (class ID = 0).
• The model processes each frame of the video to detect persons and output bounding boxes with confidence scores.

2. DeepSORT Tracker

• DeepSORT tracks objects (persons) by associating the bounding boxes from one frame to the next based on motion and appearance features.
• It also handles occlusions and re-entry of the objects into the frame.

3. OSNet for Re-identification

• OSNet is a specialized network for person re-identification. It generates unique embeddings for each detected person.
• These embeddings are used to compare and identify the same person even if the track is temporarily lost (e.g., due to occlusion).

4. Non-Maximum Suppression (NMS)

• After detecting multiple objects, NMS is applied to filter out overlapping detections and ensure that only the most confident detections remain.

5. Bounding Box and ID Assignment

• The tracker assigns a unique ID to each person detected in the video, and this ID persists across frames.
• The IDs are displayed alongside bounding boxes, which are drawn around each detected person.

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Running the Code

To run the project:

1. Ensure you have the necessary video file: Place your video in the test_videos directory or adjust the path to point to your video file.

2. Run the script:

   python person_tracking.py

3. View the output: The script will display the video with bounding boxes and unique IDs for each tracked person. It will also save the output video (output_person_tracking.mp4) to the current directory.

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Important Parameters

• CONFIDENCE_THRESHOLD = 0.5: Minimum confidence for YOLO detections. Increase this value to reduce false positives.

• NMS_THRESHOLD = 0.3: Threshold for Non-Maximum Suppression to handle overlapping bounding boxes.

• REIDENTIFY_DELAY_FRAMES = 30: Maximum number of frames to wait before attempting re-identification of a lost person track.

• DeepSORT Parameters:

  • max_age=200: Maximum number of frames an object can remain undetected before its track is removed.
  • n_init=5: Minimum number of consecutive detections before an object is confirmed as being tracked.
  • nms_max_overlap=0.5: Maximum allowed overlap for NMS in the DeepSORT tracker.

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Troubleshooting

1. ID Switching: If you notice frequent switching of person IDs, try adjusting the max_age, n_init, and nms_max_overlap parameters of the DeepSORT tracker to improve tracking consistency.

2. Overlapping Detections: If overlapping detections are an issue, increasing the NMS_THRESHOLD can help eliminate redundant bounding boxes.

3. Low FPS: If the processing speed is low, consider reducing the video resolution or switching to a faster model (e.g., a lighter version of YOLOv8).

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References

• YOLOv8: https://github.com/ultralytics/ultralytics
• DeepSORT: https://github.com/levan92/deep_sort_realtime
• OSNet: https://github.com/KaiyangZhou/deep-person-reid