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Multimodal Object Tracking System for KaAI-DD

This repository contains the code and sample dataset for tracking objects using the KaAI-DD dataset. The system combines image and LiDAR data, leveraging a Kalman filter and the Hungarian algorithm for efficient and accurate real-time tracking.

Features

  • Multimodal Data Integration: Combines image and LiDAR data for robust object tracking.
  • Kalman Filter: Used for estimating the position and velocity of tracked objects.
  • Hungarian Algorithm: Ensures optimal matching of tracked objects using cost matrices.
  • ResNet-50: Extracts feature vectors from images for improved visual tracking.

3D Object Tracking

Instructions

  1. Set Up Paths:

    • Modify the paths in the code:
      • line 51: Set to the path containing the annotated JSON files for the point cloud data.
      • line 52: Set to the path containing the image files (.png) from the forward camera (flir4).
      • line 53: Set to the path containing the LiDAR files (.pcd).

  2. Adjust Folder Name:

    • Modify the folder name in lines 207 and 208 to match the format 2024xxxx_drive_, where xxxx should be adjusted accordingly.

  3. Run the Tracking Process:

    • After making the necessary modifications, run the following command in the terminal:
    python 3D_TRACKING.py

  4. Validate Results:

    • Validate the tracking results using SustechPoint as per the annotation method described.

2D Object Tracking

Instructions

  1. Set Up Path:

    • Modify the path in line 81 to point to the directory containing the 2D labels for the forward camera.

  2. Run the Tracking Process:

    • After modifying the path, run the following command in the terminal:
    python 2D_TRACKING.py

Notes

  • Ensure all paths are correctly set up before running the tracking scripts.
  • The system is designed for real-time object tracking and is optimized for use with the KaAI-DD dataset.
  • A sample dataset has been provided in this repository to help you get started.