Skip to main content

Object detection in surveillance video from dense trajectories

Resource type
Thesis type
(Thesis) M.Sc.
Date created
Detecting objects such as humans or vehicles is a central problem in surveillance video. Myriad standard approaches exist for this problem. At their core, approaches consider either the appearance of people, patterns of their motion, or differences from the background. In this paper we build on dense trajectories, a state-of-the-art approach for describing spatio-temporal patterns in video sequences. We demonstrate an application of dense trajectories to object detection in surveillance video, showing that they can be used to both regress estimates of object locations and accurately classify objects.
Copyright statement
Copyright is held by the author.
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Mori, Greg
Member of collection
Download file Size
etd9390_MZhai.pdf 4.1 MB

Views & downloads - as of June 2023

Views: 0
Downloads: 0