Fast-Tracker 2.0：アクティブビジョンと人物位置回帰による空中追跡の自律性向上
Fast-Tracker 2.0: Improving Autonomy of Aerial Tracking with Active Vision and Human Location Regression
ドローンによる人物追跡システムを改良し、深層学習による人物検出・位置推定と360度アクティブビジョン、遮蔽を考慮した軌道計画を導入して追跡性能を高めた研究。
著者: Neng Pan, Ruibin Zhang, Tiankai Yang, Chao Xu, Fei Gao
分類: cs.RO
原文アブストラクト
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.
