Silhouette-based method for object classification and human action recognition in video

Yiǧithan Dedeoǧlu*, B. Uǧur Töreyin, Uǧur Güdükbay, A. Enis Çetin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

81 Citations (Scopus)

Abstract

In this paper we present an instance based machine learning algorithm and system for real-time object classification and human action recognition which can help to build intelligent surveillance systems. The proposed method makes use of object silhouettes to classify objects and actions of humans present in a scene monitored by a stationary camera. An adaptive background subtracttion model is used for object segmentation. Template matching based supervised learning method is adopted to classify objects into classes like human, human group and vehicle; and human actions into predefined classes like walking, boxing and kicking by making use of object silhouettes.

Original languageEnglish
Title of host publicationComputer Vision in Human-Computer Interaction - ECCV 2006 Workshop on HCI, Proceedings
PublisherSpringer Verlag
Pages64-77
Number of pages14
ISBN (Print)3540342028, 9783540342021
Publication statusPublished - 2006
Externally publishedYes
EventECCV 2006 Workshop on HCI - Graz, Austria
Duration: 13 May 200613 May 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3979 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceECCV 2006 Workshop on HCI
Country/TerritoryAustria
CityGraz
Period13/05/0613/05/06

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