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Detection, Appearance Modeling, Identification and Tracking of People in Surveillance Videos

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dc.contributor.advisor Dailey, Matthew
dc.contributor.author Jain, Sanjana
dc.contributor.other Guha, Sumanta
dc.contributor.other Ekpanyapong, Mongkol
dc.date.accessioned 2017-05-08T03:08:13Z
dc.date.available 2017-05-08T03:08:13Z
dc.date.issued 2017-05
dc.identifier.other AIT
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/852
dc.description 101 p. en_US
dc.description.abstract Applications for identifying and tracking target individuals in stores are now rapidly finding their way onto retail businesses' agendas. Many retailers would like to implement a blacklisted tracking system, and other applications include identifying members of loyalty schemes and giving rewards to special customers. The goal of the thesis is to automate the process of detecting members of a database of target individuals and assist in tracking them through crowds and occlusions. I present an automated approach to face detection, best view analysis, and face verification integrated with tracking to monitor for target individuals. I perform face detection using a custom Viola and Jones AdaBoost detection cascade to extract face images of individuals arriving at the shop. I incorporate face detection with a tracking algorithm in order to assign tracks to faces. The best view face for each individual is then obtained using entropy-based analysis, sharpness analysis, fiducial point detection, and pose estimation. Finally, I perform face verification with the target database using ``Siamese" convolutional neural network to track target individuals and raise an alert. Previous face recognition approaches have achieved high accuracy on the high quality Labeled Faces in the Wild (LFW) dataset. In my case study, I focus on blacklisted person alerting for the HomKrun coffee shop at AIT. Images obtained from surveillance cameras have lower quality. Nevertheless, I obtain acceptable results for each of the modules of my proposed system, so it will serve as a baseline for further enhancement of methods for monitoring for target individuals in surveillance videos. en_US
dc.description.sponsorship Thailand (HM King) en_US
dc.language.iso en_US en_US
dc.publisher AIT en_US
dc.subject Convolutional neural network en_US
dc.subject Deep learning en_US
dc.subject Siamese convolutional neural network en_US
dc.subject Caffe en_US
dc.subject Face detection en_US
dc.subject Best-view analysis en_US
dc.subject Fiducial point detection en_US
dc.subject Entropy-based analysis en_US
dc.subject Sharpness analysis en_US
dc.subject Pose estimation en_US
dc.subject Face verification en_US
dc.subject Blacklisted classification en_US
dc.title Detection, Appearance Modeling, Identification and Tracking of People in Surveillance Videos en_US
dc.type Thesis en_US


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