NDLI: Action detection using multiple spatial-temporal interest point features

Content Provider	IEEE Xplore Digital Library
Author	Liangliang Cao YingLi Tian Zicheng Liu Yao, B. Zhengyou Zhang Huang, T.S.
Copyright Year	2010
Description	Author affiliation: Department of Statistics, University of California, Los Angeles (Yao, B.) \|\| Microsoft Research, Redmond (Zicheng Liu; Zhengyou Zhang) \|\| Department of Electrical Engineering, City College of New York (YingLi Tian) \|\| Beckman Institute, University of Illinois at Urbana-Champaign (Liangliang Cao; Huang, T.S.)
Abstract	This paper considers the problem of detecting actions from cluttered videos. Compared with the classical action recognition problem, this paper aims to estimate not only the scene category of a given video sequence, but also the spatial-temporal locations of the action instances. In recent years, many feature extraction schemes have been designed to describe various aspects of actions. However, due to the difficulty of action detection, e.g., the cluttered background and potential occlusions, a single type of features cannot solve the action detection problems perfectly in cluttered videos. In this paper, we attack the detection problem by combining multiple Spatial-Temporal Interest Point (STIP) features, which detect salient patches in the video domain, and describe these patches by feature of local regions. The difficulty of combining multiple STIP features for action detection is two folds: First, the number of salient patches detected by different STIP methods varies across different salient patches. How to combine such features is not considered by existing fusion methods [13] [5]. Second, the detection in the videos should be efficient, which excludes many slow machine learning algorithms. To handle these two difficulties, we propose a new approach which combines Gaussian Mixture Model with Branch-and-Bound search to efficiently locate the action of interest. We build a new challenging dataset for our action detection task, and our algorithm obtains impressive results. On classical KTH dataset, our method outperforms the state-of-the-art methods.
Starting Page	340
Ending Page	345
File Size	2191570
Page Count	6
File Format	PDF
ISBN	9781424474912
ISSN	1945788X
e-ISBN	9781424474936
DOI	10.1109/ICME.2010.5583562
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-19
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Videos Feature extraction Computer vision Conferences Adaptation model Humans Computational modeling
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Local spatio-temporal interest point detection for human action recognition

Action Recognition Using Spatial-Temporal Context

Action recognition using hybrid spatio-temporal bag-of-features

Dominant spatio-temporal modulations and energy tracking in videos: Application to interest point detection for action recognition

Action detection using multiple spatial-temporal interest point features (2010)

Action detection using multiple spatial-temporal interest point features (2010)

An unsupervised method for anomaly detection from crowd videos

Towards a Robust Spatio-Temporal Interest Point Detection for Human Action Recognition

Hierarchical codebook background model using haar-like features

Action detection using multiple spatial-temporal interest point features

Similar Documents

Local spatio-temporal interest point detection for human action recognition

Action Recognition Using Spatial-Temporal Context

Action recognition using hybrid spatio-temporal bag-of-features

Dominant spatio-temporal modulations and energy tracking in videos: Application to interest point detection for action recognition

Action detection using multiple spatial-temporal interest point features (2010)

Action detection using multiple spatial-temporal interest point features (2010)

An unsupervised method for anomaly detection from crowd videos

Towards a Robust Spatio-Temporal Interest Point Detection for Human Action Recognition

Hierarchical codebook background model using haar-like features

Action detection using multiple spatial-temporal interest point features