Integration of the 3D Environment for UAV Onboard Visual Object Tracking

TL;DR

This paper presents a novel UAV visual object tracking method that integrates 3D scene reconstruction to improve robustness against occlusion, small objects, and camera motion, outperforming traditional image-based approaches.

Contribution

It introduces a pipeline combining 3D scene reconstruction with detection-by-tracking, enhancing tracking stability and accuracy during UAV flight.

Findings

Outperforms image-based tracking methods in UAV scenarios

Improves handling of occlusion and background clutter

Enhances tracking stability during ego-motion

Abstract

Single visual object tracking from an unmanned aerial vehicle (UAV) poses fundamental challenges such as object occlusion, small-scale objects, background clutter, and abrupt camera motion. To tackle these difficulties, we propose to integrate the 3D structure of the observed scene into a detection-by-tracking algorithm. We introduce a pipeline that combines a model-free visual object tracker, a sparse 3D reconstruction, and a state estimator. The 3D reconstruction of the scene is computed with an image-based Structure-from-Motion (SfM) component that enables us to leverage a state estimator in the corresponding 3D scene during tracking. By representing the position of the target in 3D space rather than in image space, we stabilize the tracking during ego-motion and improve the handling of occlusions, background clutter, and small-scale objects. We evaluated our approach on prototypical image sequences, captured from a UAV with low-altitude oblique views. For this purpose, we adapted an existing dataset for visual object tracking and reconstructed the observed scene in 3D. The experimental results demonstrate that the proposed approach outperforms methods using plain visual cues as well as approaches leveraging image-space-based state estimations. We believe that our approach can be beneficial for traffic monitoring, video surveillance, and navigation.