Embedded UAV Real-Time Visual Object Detection and Tracking

Abstract

The use of camera-equipped Unmanned Aerial Vehicles (UAVs, or “drones”) for a wide range of aerial video capturing applications, including media production, surveillance, search and rescue operations, etc., has exploded in recent years. Technological progress has led to commercially available UAVs with a degree of cognitive autonomy and perceptual capabilities, such as automated, on-line detection and tracking of target objects upon the captured footage. However, the limited computational hardware, the possibly high camera-to-target distance and the fact that both the UAV/camera and the target(s) are moving, makes it challenging to achieve both high accuracy and stable real-time performance. In this paper, the current state-of-the-art on real-time object detection/tracking is overviewed. Additionally, a relevant, modular implementation suitable for on-drone execution (running on top of the popular Robot Operating System) is presented and empirically evaluated on a number of relevant datasets. The results indicate that a sophisticated, neural network-based detection and tracking system can be deployed at real-time even on embedded devices.