# Fast-Tracker 2.0: Improving Autonomy of Aerial Tracking with Active Vision and Human Location Regression

arXiv: https://arxiv.org/abs/2103.06522 · HTML: https://mlsanity.com/paper/2103.06522/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2021.

## Built on by (most cited first, 2 of 2)

- [Open-World Drone Active Tracking with Goal-Centered Rewards](https://mlsanity.com/paper/2412.00744/) (2024)
- [Instance-level Visual Active Tracking with Occlusion-Aware Planning](https://mlsanity.com/paper/2604.21453/) (2026)

## Abstract (arXiv)

In recent years, several progressive works promote the development of aerial tracking. One of the representative works is our previous work Fast-tracker which is applicable to various challenging tracking scenarios. However, it suffers from two main drawbacks: 1) the over simplification in target detection by using artificial markers and 2) the contradiction between simultaneous target and environment perception with limited onboard vision. In this paper, we upgrade the target detection in Fast-tracker to detect and localize a human target based on deep learning and non-linear regression to solve the former problem. For the latter one, we equip the quadrotor system with 360 degree active vision on a customized gimbal camera. Furthermore, we improve the tracking trajectory planning in Fast-tracker by incorporating an occlusion-aware mechanism that generates observable tracking trajectories. Comprehensive real-world tests confirm the proposed system's robustness and real-time capability. Benchmark comparisons with Fast-tracker validate that the proposed system presents better tracking performance even when performing more difficult tracking tasks.