Vision-Based Target Detection and Tracking System for a Quadcopter

Abstract

Tracking a maneuvering target with a quadcopter is a challenging problem, that involves a variety of fields such as visual tracking, state estimation, and control algorithms. Most existing unmanned aerial vehicle (UAV) systems fail to track targets accurately in the long term and cannot relocate targets after target loss. This paper aims to design and implement a vision-based target tracking system for quadcopters that can steadily and accurately track the ground target as well as the air target without any prior information. We employ a vision detection algorithm to select the target quickly and precisely. To fit complex practical conditions, a target tracking algorithm is developed based on correlation filters, which is capable of tracking targets with large-scale variation and fast motion. In addition, an efficient redetection algorithm based on the support vector machine (SVM) is designed to handle target occlusions and loss. The target states are estimated from the visual information by an improved Lucas-Kanade (LK) optical flow method and an extended Kalman filter (EKF). Moreover, a double closed-loop Proportion Integral Differential (PID) controller using the estimated states is designed to follow the target. By implementing the main algorithms on an onboard NUC computer, an extensive outdoor flight is evaluated for a quadcopter platform equipped with a stereo camera. The experimental results validate the feasibility and practicability of the developed system.