dc.contributor.authorChen, Jiaying
dc.date.accessioned2017-05-16T01:24:52Z
dc.date.available2017-05-16T01:24:52Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10356/71294
dc.description.abstractPeople detection and tracking in videos have a wide variety of applications in computer vision such as surveillance, people recognition, crowd behavior analysis, human-machine interaction. In this paper, I present some methods for people detection and tracking. Firstly, compared to the original HOG-SVM classifier without hard examples, I used the HOG-SVM classifier with pre-trained hard examples for people detection. Secondly, I proposed a novel method for people detection and tracking. Proposed approach utilizes the deformable part model (DPM) object detector to get people features and detect people positions in the video as well as high speed tracking with kernelized correlation filter (KCF) based tractor to tract the detected person.en_US
dc.format.extent80 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titlePeople detection and tracking in videosen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorYuan Junsongen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US


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