# D-VAT: End-to-End Visual Active Tracking for Micro Aerial Vehicles

arXiv: https://arxiv.org/abs/2308.16874 · HTML: https://mlsanity.com/paper/2308.16874/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2023.

## Built on by (most cited first, 6 of 6)

- [VLM Can Be a Good Assistant: Enhancing Embodied Visual Tracking with Self-Improving Vision-Language Models](https://mlsanity.com/paper/2505.20718/) (2025)
- [Open-World Drone Active Tracking with Goal-Centered Rewards](https://mlsanity.com/paper/2412.00744/) (2024)
- [UAV-Track VLA: Embodied Aerial Tracking via Vision-Language-Action Models](https://mlsanity.com/paper/2604.02241/) (2026)
- [Instance-level Visual Active Tracking with Occlusion-Aware Planning](https://mlsanity.com/paper/2604.21453/) (2026)
- [Leveraging Event Streams with Deep Reinforcement Learning for End-to-End UAV Tracking](https://mlsanity.com/paper/2410.14685/) (2024)
- [CosFly-VLA: A Spatially Aware Vision-Language-Action Model for UAV Tracking](https://mlsanity.com/paper/2607.15004/) (2026)

## Abstract (arXiv)

Visual active tracking is a growing research topic in robotics due to its key role in applications such as human assistance, disaster recovery, and surveillance. In contrast to passive tracking, active tracking approaches combine vision and control capabilities to detect and actively track the target. Most of the work in this area focuses on ground robots, while the very few contributions on aerial platforms still pose important design constraints that limit their applicability. To overcome these limitations, in this paper we propose D-VAT, a novel end-to-end visual active tracking methodology based on deep reinforcement learning that is tailored to micro aerial vehicle platforms. The D-VAT agent computes the vehicle thrust and angular velocity commands needed to track the target by directly processing monocular camera measurements. We show that the proposed approach allows for precise and collision-free tracking operations, outperforming different state-of-the-art baselines on simulated environments which differ significantly from those encountered during training. Moreover, we demonstrate a smooth real-world transition to a quadrotor platform with mixed-reality.