Real-time Aerial Detection and Reasoning on Embedded-UAVs
Tin Lai
We present a unified pipeline architecture for a real-time detection system
on an embedded system for UAVs. Neural architectures have been the industry
standard for computer vision. However, most existing works focus solely on
concatenating deeper layers to achieve higher accuracy with run-time
performance as the trade-off. This pipeline of networks can exploit the
domain-specific knowledge on aerial pedestrian detection and activity
recognition for the emerging UAV applications of autonomous surveying and
activity reporting. In particular, our pipeline architectures operate in a
time-sensitive manner, have high accuracy in detecting pedestrians from various
aerial orientations, use a novel attention map for multi-activities
recognition, and jointly refine its detection with temporal information.
Numerically, we demonstrate our model's accuracy and fast inference speed on
embedded systems. We empirically deployed our prototype hardware with full live
feeds in a real-world open-field environment.