Project Proposal: Data-Driven Feature Tracking for Aerial Imagery

Overview

This project explores the application of a data-driven feature tracking method, initially designed for event cameras, to aerial imagery. Our objective is to extract and track visual features over a sequence of aerial images, using these tracks to estimate 3D camera poses through a Structure-from-Motion (SfM) algorithm. In the end, we aim to evaluate the accuracy and reliability of the reconstructed camera poses, pushing forward the integration of event camera techniques into aerial imagery analysis.

Objectives

1. Develop a Robust Feature Tracking Pipeline
   We will adapt data-driven feature tracking techniques from event camera research to aerial imagery, creating a robust tracking pipeline to handle the unique dynamics and resolutions involved.

2. Feature Tracks and SfM Integration
   Using the feature tracking data, we will build feature tracks over the image sequence, integrate these tracks with an SfM algorithm, and estimate 3D camera poses.

3. Quality Evaluation of 3D Poses
   By analyzing our reconstructed camera poses, we aim to ensure high accuracy and reliability across different environmental conditions, identifying the strengths and limitations of our adapted approach.

Dataset

Our dataset includes aerial imagery captured through both event cameras and RGB cameras, providing a rich basis for feature extraction and tracking comparisons across modalities.

Methodology

1. Feature Extraction from Aerial Imagery

   • We will adapt the feature tracking method proposed by Messikommer et al. (2023), originally tailored for event cameras, as our baseline approach.
   • The process involves adapting event-based tracking techniques to work efficiently with aerial imagery, especially to capitalize on the high temporal resolution offered by event camera data.

2. Feature Tracking Across the Image Sequence

   • Features will be tracked across consecutive images to establish consistent feature trajectories, allowing us to analyze motion across the sequence.
   • We will utilize a novel frame attention module, introduced by Messikommer et al., to improve tracking robustness and information sharing within each frame.

3. Generating Feature Tracks for SfM

   • We will generate input feature tracks for the SfM algorithm, ensuring precision and consistency of tracked features over the sequence to support robust 3D pose estimation.

4. 3D Pose Estimation Using SfM

   • Using an SfM algorithm like COLMAP or BA4S, we will estimate 3D camera poses from the feature tracks, testing the adaptability of the data-driven tracking pipeline in a spatial context.

5. Evaluation of Results

   • We will measure the accuracy of the estimated camera poses by comparing track consistency and robustness across various environmental conditions.

Expected Outcomes

• Feature Tracking Pipeline: A robust, adaptive tracking pipeline for aerial imagery leveraging event camera technology.
• Evaluation Report: Detailed analysis of feature tracking accuracy, camera pose estimation quality, and adaptability across scenarios.

References

Messikommer, N., et al., "Data-Driven Feature Tracking for Event Cameras," CVPR 2023.