Unsupervised learning of human action categories using spatial-temporal words

Unsupervised learning of human action categories using spatial-temporal words (2006)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by Juan Carlos Niebles , Hongcheng Wang , Li Fei-fei

Venue:	In Proc. BMVC
Citations:	161 - 4 self

Versions

Version History

Metadata Version 1

Datum	Value	Source
TITLE	Unsupervised learning of human action categories using spatial-temporal words	INFERENCE
AUTHOR NAME	Juan Carlos Niebles	SVM HeaderParse 0.2
AUTHOR AFFIL	1 University of Illinois at Urbana-Champaign, Urbana	SVM HeaderParse 0.2
AUTHOR ADDR	IL 61801, USA; 2 Universidad del Norte, Barranquilla, Colombia	SVM HeaderParse 0.2
AUTHOR NAME	Hongcheng Wang	SVM HeaderParse 0.2
AUTHOR AFFIL	1 University of Illinois at Urbana-Champaign, Urbana	SVM HeaderParse 0.2
AUTHOR ADDR	IL 61801, USA; 2 Universidad del Norte, Barranquilla, Colombia	SVM HeaderParse 0.2
AUTHOR NAME	Li Fei-fei	SVM HeaderParse 0.2
AUTHOR AFFIL	1 University of Illinois at Urbana-Champaign, Urbana	SVM HeaderParse 0.2
AUTHOR ADDR	IL 61801, USA; 2 Universidad del Norte, Barranquilla, Colombia	SVM HeaderParse 0.2
ABSTRACT	Imagine a video taken on a sunny beach, can a computer automatically tell what is happening in the scene? Can it identify different human activities in the video, such as water surfing, people walking and lying on the beach? To automatically classify or localize different actions in video sequences is very useful for a variety of tasks, such as video surveillance, objectlevel video summarization, video indexing, digital library organization, etc. However, it remains a challenging task for computers to achieve robust action recognition due to cluttered background, camera motion, occlusion, and geometric and photometric variances of objects. For example, in a live video of a skating competition, the skater moves rapidly across the rink, and the camera also moves to follow the skater. With moving camera, non-stationary background, and moving target, few vision algorithms could identify, categorize and	SVM HeaderParse 0.2
YEAR	2006	INFERENCE
VENUE	In Proc. BMVC	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	3 found	ParsCit 1.0