Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/71294
Title: People detection and tracking in videos
Authors: Chen, Jiaying
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2017
Abstract: People 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.
URI: http://hdl.handle.net/10356/71294
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
Chen Jiaying_FYP report.pdf
  Restricted Access
main article38.76 MBAdobe PDFView/Open

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.