  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.
