Key-Frame Analysis for Face Related Video on GPU-Accelerated Embedded Platform
Xuan Qi, Chen Liu, Stephanie Schuckers · 2016
Since video monitoring cameras now are implemented widely, video analytics has drawn much attention as a new research area. Correspondingly, there is an emerging need to detect faces through a period of video or a great number of videos to make comparisons with the stored identities in the database for personal identification or other purposes. Thus, face detection in video has gained great attention. However, when there are lots of videos or when the video is very long, the workload for face detection becomes very huge. As a result, the detecting time is prolonged. Thus, there is a need to detect faces quickly with reduced computation time. In this paper, we added key frame analysis into the face detection process and employed the mobile graphic processing unit (GPU) platform to improve the overall performance. Our experimental results show that our system is capable of handling relative complex scenarios and achieve high success rate. Our performance analysis shows the potential in applying GPU towards this type of applications to improve the overall processing speed.