Promote Sign in Sign up

Repository

Object-Detection-and-Tracking

Human Tracking System: Multi-tracker detection and tracking system with YOLOv12 and BoxMOT, optimized for dance videos with mobile deployment capabilities.

hamzashafiq28
Repository
Argument
Description
--video
Path to input video
--output
Path to output video (processed)
--tracker
Tracker type to use ( deepocsort , boosttrack , strongsort , botsort , bytetrack )

Specs

  • ArgumentDescription
  • --videoPath to input video
  • --outputPath to output video (processed)
  • --trackerTracker type to use ( deepocsort , boosttrack , strongsort , botsort , bytetrack )
  • --modelPath to YOLO model
  • --confConfidence threshold for detections (0.0-1.0)
  • --deviceDevice to use for inference ( cuda , cpu )
  • --no-showDisable visualization during processing
  • --no-trajectoryDisable trajectory visualization
  • ArgumentDescription
  • --optimize-mobileOptimize and export model for mobile devices
  • --export-formatExport format ( onnx , tflite , coreml , torchscript )
  • --int8Use INT8 quantization for smaller model size
  • --optimize-onlyOnly optimize/export model without running tracking
  • --export-pathDirectory to save exported model

README

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).