Self-Adaptive Machine Learning Operating Systems for Security Applications

Kyeong Seon Kim, Dohyun Kwon, Yejin Kim, Junhui Kim, Joongheon Kim

Abstract: This paper proposes a reliable and self-adaptive operating system management policy for CCTV-based security applications which controls arrival image compression rates. After receiving image sequences via CCTV cameras, the system enqueues the sequences of images and processes them for face recognition. High compression rates in CCTV-recorded images provide low recognition performance due to quantization while it is beneficial in terms of queue stability. On the other hand, low compression rates in the images provide high recognition performance while it may introduce queue overflows. Therefore, this paper designs a queue-aware, self-adaptive, and reliable operating system management scheme which aims at face identification performance maximization while avoiding queue overflow by controlling CCTV-recorded image compression rates based on the theory of Lyapunov optimization.