CamAspect: An Intelligent Automated Real-Time Surveillance System With Smartphone Indexing

In this letter, the authors propose a novel approach to develop an intelligent automated real-time surveillance system based solely on efficient tracking and facial recognition. With this architecture, we aim at compensating for the deficiencies of face-recognition-based surveillance methods that are computationally intractable or unreliable to be deployed for real-time automated surveillance. The presented work aims to construct a standalone video surveillance system, which is capable of detecting, identifying, and tracking persons in a video feed, snapshotting interesting physical feature changes, and transmitting this information along with the snapshots and identification parameter to the user. Specifically, we adopt a computationally cheap and accurate object tracker Deep SORT and combine it with a very accurate and computationally heavy facial recognition model FaceNet. The proposed method shows a 115% increase in runtime for the aforementioned combined architecture. The system also consists of a smartphone interface for efficient searching and indexing of identifications across timestamps.