Intensity classification background model based on the tracing scheme for deep learning based CCTV pedestrian detection

Abstract

This study has suggested an image analysis system based on the Deep Learning for CCTV pedestrian detection and tracing improvement and did experiments for objective verification by designing study model and evaluation model. The study suggestion is that if someone's face did not be recognized in crime scene CCTV footage, the same pedestrian would be traced and found in other image data from other CCTV by using Color Intensity Classification method for clothes colors as body features and body fragmentation technique into 7 parts (2 arms, 2 legs, 1 body, 1 head, and 1 total). If one of other CCTV footage has recorded its face, the identity of the person would be secured. It is not only detection but also search from stored bulk storage to prevent accidents or cope with them in advance by cost reduction of manpower and a fast response. Therefore, CIC7P(Color Intensity Classification 7 Part Base Model) had been suggested by learning device such as Machine Learning or Deep Learning to improve accuracy and speed for pedestrian detection and tracing. In addition, the study has proved that it is an advanced technique in the area of pedestrian detection through experimental proof.