🤖 AI Summary
This work addresses the challenges of visual object tracking on unmanned aerial vehicles (UAVs), where dynamic platforms, camera motion, and limited computational resources often compromise robustness or incur excessive computational overhead. The authors propose the Modular Asynchronous Tracking Architecture (MATA), which integrates a Transformer-based tracker with an extended Kalman filter, leveraging sparse optical flow for ego-motion compensation and target trajectory modeling. To better evaluate real-world embedded deployment, they introduce a hardware-agnostic evaluation protocol tailored for embedded systems and a novel metric, Normalized Time to Failure (NT2F). Experiments on benchmarks such as UAV123 demonstrate significant improvements in both Success and NT2F scores. Furthermore, real-time deployment on a Jetson AGX Orin platform using ROS 2 validates that the proposed evaluation protocol accurately reflects practical embedded performance.
📝 Abstract
Object tracking from Unmanned Aerial Vehicles (UAVs) is challenged by platform dynamics, camera motion, and limited onboard resources. Existing visual trackers either lack robustness in complex scenarios or are too computationally demanding for real-time embedded use. We propose an Modular Asynchronous Tracking Architecture (MATA) that combines a transformer-based tracker with an Extended Kalman Filter, integrating ego-motion compensation from sparse optical flow and an object trajectory model. We further introduce a hardware-independent, embedded oriented evaluation protocol and a new metric called Normalized time to Failure (NT2F) to quantify how long a tracker can sustain a tracking sequence without external help. Experiments on UAV benchmarks, including an augmented UAV123 dataset with synthetic occlusions, show consistent improvements in Success and NT2F metrics across multiple tracking processing frequency. A ROS 2 implementation on a Nvidia Jetson AGX Orin confirms that the evaluation protocol more closely matches real-time performance on embedded systems.