Fast‐Tracker 2.0: Improving autonomy of aerial tracking with active vision and human location regression

Abstract

Abstract In recent years, several progressive studies promote the development of aerial tracking. One of the representative studies is our previous work Fast‐Tracker which is applicable to various challenging tracking scenarios. However, it suffers from two main drawbacks: (1) the oversimplification in target detection by using artificial markers and (2) the contradiction between simultaneous target and environment perception with limited onboard vision. In this study, we upgrade the target detection in Fast‐Tracker to detect and localise 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° active vision on a customised 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. The cover image is based on the Original Article Fast‐Tracker 2.0: Improving autonomy of aerial tracking with active vision and human location regression by Can Cui et al., https://doi.org/10.1049/csy2.12033.

Keywords

• cameras
• feature extraction
• filtering theory
• learning (artificial intelligence)
• object detection
• regression analysis