Human Tracker

A modular human tracking system using multiple tracker types from BoxMOT.

Features

• Support for multiple trackers: DeepOCSORT, BoostTrack, StrongSORT, BoTSORT, ByteTrack
• Optimized parameters for dancing scenarios with occlusions
• Visualization of tracks and trajectories
• Performance statistics
• Mobile optimization with model export to various formats

Installation Instructions

Setting Up the Environment

Clone the repository

git clone https://github.com/hamzashafiq28/Object_Detection_and_Tracking.git
cd Object_Detection_and_Tracking

Create and activate a virtual environment

Using venv (Python's built-in module):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Using conda:

conda create -n tracker_env python=3.10
conda activate tracker_env

Install required dependencies

pip install -r requirements.txt

Download YOLOv12 model (if not downloaded automatically)

Make sure yolo12n.pt is available or specify your model path using the --model argument.

Download ReID model for trackers (optional, improves tracking)

Refer to BoxMOT documentation for ReID model download instructions.

Usage

Basic Usage

python track.py --video path/to/video.mp4

Command Line Arguments

Argument	Description	Default
--video	Path to input video	1.mp4
--output	Path to output video (processed)	1_out.mp4
--tracker	Tracker type to use (deepocsort, boosttrack, strongsort, botsort, bytetrack)	deepocsort
--model	Path to YOLO model	yolo8n.pt
--conf	Confidence threshold for detections (0.0-1.0)	0.6
--device	Device to use for inference (cuda, cpu)	cuda
--no-show	Disable visualization during processing	False
--no-trajectory	Disable trajectory visualization	False

Mobile Optimization Arguments

Argument	Description	Default
--optimize-mobile	Optimize and export model for mobile devices	False
--export-format	Export format (onnx, tflite, coreml, torchscript)	onnx
--int8	Use INT8 quantization for smaller model size	False
--optimize-only	Only optimize/export model without running tracking	False
--export-path	Directory to save exported model	mobile_models

Examples

• Process video with default settings (DeepOCSORT tracker):

python main.py --video path/to/video.mp4

• Change tracker to BoostTrack for better occlusion handling:

python main.py --video path/to/video.mp4 --tracker boosttrack

• Process video on CPU with ByteTrack (fastest tracker):

python main.py --video path/to/video.mp4 --tracker bytetrack --device cpu

• Higher detection confidence for cleaner results:

python main.py --video path/to/video.mp4 --conf 0.8

• Process video without displaying (batch processing):

python main.py --video path/to/video.mp4 --no-show

• For resource-constrained devices:

python main.py --video path/to/video.mp4 --tracker bytetrack --no-trajectory

• Optimize model for mobile deployment (ONNX):

python main.py --optimize-mobile --export-format onnx --optimize-only

• Optimize model to TFLite format with INT8 quantization:

python main.py --optimize-mobile --export-format tflite --int8 --optimize-only

• Export to CoreML format for iOS deployment:

python main.py --optimize-mobile --export-format coreml --optimize-only

• Export model and run tracking with it:

python main.py --video path/to/video.mp4 --optimize-mobile --tracker bytetrack

Performance Considerations

• GPU acceleration is highly recommended for real-time performance.
• BoostTrack and DeepOCSORT provide the best tracking quality for dance videos with complex movements.
• ByteTrack is the fastest tracker but may struggle with complex occlusions.
• Disable trajectory visualization (--no-trajectory) for better performance on low-end devices.
• Adjust confidence threshold based on scene complexity (lower for challenging scenes, higher for cleaner output).

Mobile Optimization

The system supports exporting optimized models for mobile deployment:

• ONNX format: Compatible with various frameworks and mobile inference engines
• TFLite format: Optimized for TensorFlow Lite on Android devices
• CoreML format: Native support for iOS devices
• TorchScript format: For PyTorch Mobile deployment

Optimization techniques include:

• Reduced input resolution (320×320) for faster inference
• INT8 quantization option for up to 70% smaller model size
• Graph simplification for more efficient execution

Requirements

• Python 3.8 or newer
• PyTorch 1.8 or newer
• BoxMOT library
• Ultralytics YOLO
• OpenCV
• CUDA-capable GPU (recommended)
• ONNX, ONNX Runtime (for optimization)

Troubleshooting

• ReID model missing: If you get an error about missing ReID model weights, download it manually as described in the installation steps.
• CUDA out of memory: Try reducing batch size or switch to CPU mode with --device cpu.
• Slow performance: Consider using ByteTrack tracker and disabling trajectory visualization.
• Detection issues: Adjust the confidence threshold with --conf parameter (lower for more detections).
• Mobile model issues: When using exported models, ensure image size is set to match the export size (320×320).
• Export errors: Make sure required packages are installed (pip install onnx onnxruntime).