A Study on People Counting and Positioning Based on Image Recognition Using Disaster Scenario

• Main Authors: Ming Shiuan, Fan, 范銘軒
• Other Authors: 蔡旭昇
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/c3zm85

碩士 === 樹德科技大學 === 資訊管理系碩士班 === 106 === In the event of an accident, the people fled in order to survive. The route of their journey was greatly affected by the environment and the surrounding conditions. The accident was often accompanied by smoke caused by various factors, which made it impossible to know effectively through the on-site surveillance camera. Personnel travel and escape, the personnel may be different in body shape or body state, and the moving process has different speeds of movement and collision with the push and the person in the image overlaps, so that the subsequent analysis and research on its travel dynamics Not easy. This study uses the artificial intelligence deep learning training model combined with image recognition technology to divide the Gongge area into 5 square meters for people to move in their area, and place smoke cakes for smoking, create a smoke scene of the disaster scene, and simulate The indoor camera monitors the camera, while the inverted U-shaped route simulates the overlap between the person''s escape and the obscuration of the smoke mask for image recognition in different route segments. The captured image is obtained by Tensorflow''s Object Detection API combined with SSD and Fast R-CNN training algorithms to detect the impact of smoke screen on personnel image recognition. After processing the frame image and data The chart shows that there is a significant difference in the recognition rate between the two, and the Fast R-CNN model is used to take the number and position information from the image from the comparison results. It is hoped that the research will improve the current disaster prevention route planning and post-disaster analysis. Integrity.