Open-World Drone Active Tracking with Goal-Centered Rewards

TL;DR

This paper introduces DAT, a comprehensive open-world drone tracking benchmark, and GC-VAT, a reinforcement learning method with goal-centered rewards and curriculum training, significantly improving drone active tracking in complex environments.

Contribution

It pioneers the first open-world drone tracking benchmark and proposes a novel RL method with goal-centered rewards and curriculum learning for enhanced performance.

Findings

Achieved approximately 72% tracking success rate on the simulator.

Demonstrated superior performance of GC-VAT over existing methods.

Provided a digital twin tool for unlimited scene generation.

Abstract

Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations, providing a more practical solution for effective tracking in dynamic environments. However, accurate Drone Visual Active Tracking using reinforcement learning remains challenging due to the absence of a unified benchmark and the complexity of open-world environments with frequent interference. To address these issues, we pioneer a systematic solution. First, we propose DAT, the first open-world drone active air-to-ground tracking benchmark. It encompasses 24 city-scale scenes, featuring targets with human-like behaviors and high-fidelity dynamics simulation. DAT also provides a digital twin tool for unlimited scene generation. Additionally, we propose a novel reinforcement learning method called GC-VAT, which aims to improve the performance of drone tracking targets in complex scenarios. Specifically, we design a Goal-Centered Reward to provide precise feedback across viewpoints to the agent, enabling it to expand perception and movement range through unrestricted perspectives. Inspired by curriculum learning, we introduce a Curriculum-Based Training strategy that progressively enhances the tracking performance in complex environments. Besides, experiments on simulator and real-world images demonstrate the superior performance of GC-VAT, achieving a Tracking Success Rate of approximately 72% on the simulator. The benchmark and code are available at https://github.com/SHWplus/DAT_Benchmark.