none

• Main Authors: Qi-Lun Hong, 洪啓綸
• Other Authors: Wen-June Wang
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/z75uhu

碩士 === 國立中央大學 === 電機工程學系 === 106 === This thesis attempts to design and implement a multiple human tracking and identification system that is applicable to omnidirectional surveillance by a fisheye camera. In this work, a top-view fisheye camera is mounted on the ceiling. Image distortion correction is not performed on each image frame because the human objects far from the image center might be cropped after lens undistortion. The appearance of human varies depending on the position in an image, and thus it is hard to detect and recognize human by traditional approaches. Accordingly, the deep learning technique developed rapidly in recent years is employed for achieving efficient human tracking and recognition. By learning from a big amount of training images, the computer will have the ability of extracting features, detecting and identifying human. The main algorithm of the proposed deep neural network includes two stages of networks. The first part is a YOLO-based deep architecture for detecting and locating human objects. The second part combines location and appearance information to track an identical person. Through the entire process of tracking a person, the identification will be kept as the same by using a so-called AlignedReID network. From the experimental results, the efficiency and robustness of the proposed algorithm have been verified.