An Improved Deep-Layer Architecture for Real-Time End-to-End Person Recognition System

Surveillance of human activities in real time has drawn tremendous attention in the field of research. As manual monitoring of surveillance videos is expensive and prone to error, automation of surveillance is preferred. Person recognition is one of the fundamental problems related to automation of surveillance. It is defined as the system that generates correspondence between two images captured by different cameras at different times. Matching of probe image with the people in the surveillance video is really challenging due to variations in background, costume of people, pose, camera views, lighting, etc. A deep-learning-based end-end person recognition system is proposed to suit the real-world environment. This paper discusses the architecture of proposed system with the issues encountered during the implementation. Experiments were conducted based on different situations to illustrate the results of the proposed system with suitable evaluation metric. CUHK03 dataset was used for experiment. Real-time data were collected and tested to prove the robustness of the proposed system.