Outdoor target tracking UAV (Unmanned Aerial Vehicle), which is a research hotspot in the field of computer vision and unmanned aerial system, needs robust target-tracking algorithms with good real-time performance, accurate position estimator of UAV and the corresponding control strategy of the system. In this paper, we designed an outdoor drone tracking system using PCA (Principal Component Analysis) face recognition algorithm and KCF (Kernel Correlation Filter) target tracking algorithm. Firstly, an image acquisition unit is constructed by using an on-board pan-and-tilt camera to capture an outdoor monitored area. Secondly, the PCA algorithm is used for face matching, then the tracking mode is automatically transferred when the expected face target is recognized. Finally, the target tracking is performed by the KCF algorithm. After that, the position error is calculated and sent to the flight control system through the MavLink protocol, thereby performing posture adjustment and completing the tracking and monitoring task. Experimental results show that the performance of outdoor target tracking flight robot is stable and reliable, which meets the requirements of outdoor target tracking and has a good application prospect.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.