3D Voxel-based Person Tracking
Project Overview
This project aims to develop a robust system for multi-person tracking using 3D voxel reconstruction and color modeling. The system operates in two phases: an offline phase for creating personalized color models, and an online phase for real-time tracking and labeling.
Key Objectives
- Create individual color models for each person in an offline process
- Label voxels in real-time based on the pre-computed color models
Methodology
Offline Phase
- Camera Calibration: Ensure accurate spatial relationships between multiple cameras
- 3D Reconstruction: Convert 2D frames into a 3D voxel representation
- Voxel Clustering: Apply K-means clustering to group voxels by position
- Color Model Generation: Project voxels onto camera views to create person-specific color models
Online Phase
- Real-time Voxel Model: Construct voxel model from incoming frames
- Voxel Clustering: Group voxels using the established clustering method
- Color Model Matching: Compare online color models with offline models
- Position Tracking: Record 2D floor positions of clusters over time
Key Features
- Multi-camera setup for comprehensive spatial coverage
- 3D voxel reconstruction for robust spatial representation
- K-means clustering for efficient voxel grouping
- Gaussian Mixture Models (GMMs) for color modeling
- Real-time tracking and labeling of multiple persons
Technologies Used
- OpenCV for image processing and camera calibration
- scikit-learn for K-means clustering and GMM implementation
- Matplotlib for visualization of tracking results
- Scipy
Challenges and Solutions
- Occlusion Handling: The 3D voxel approach helps in managing partial occlusions
- Real-time Performance: Efficient algorithms and optimized code ensure real-time processing
- Color Ambiguity: Person-specific color models improve distinction between similarly clothed individuals