NDLI: Foreground detection using spatiotemporal projection kernels

Content Provider	IEEE Xplore Digital Library
Author	Moshe, Y. Hel-Or, H. Hel-Or, Y.
Copyright Year	2012
Description	Author affiliation: Department of Computer Science, University of Haifa, Haifa, 31905, Israel (Moshe, Y.; Hel-Or, H.) \|\| School of Computer Science, The Interdisciplinary Center, Kanfey Nesharim St., Herzliya, 46150, Israel (Hel-Or, Y.)
Abstract	In this paper, we propose a novel video foreground detection method that exploits the statistics of 3D spacetime patches. 3D space-time patches are characterized by means of the subspace they span. As the complexity of real-time systems prohibits performing this modeling directly on the raw pixel data, we propose a novel framework in which spatiotemporal data is sequentially reduced in two stages. The first stage reduces the data using a cascade of linear projections of 3D space-time patches onto a small set of 3D Walsh-Hadamard (WH) basis functions known for its energy compaction of natural images and videos. This stage is efficiently implemented using the Gray-Code filtering scheme [2] requiring only 2 operations per projection. In the second stage, the data is further reduced by applying PCA directly to the WH coefficients exploiting the local statistics in an adaptive manner. Unlike common techniques, this spatiotemporal adaptive projection exploits window appearance as well as its dynamic characteristics. Tests show that the proposed method outperforms recent foreground detection methods and is suitable for real-time implementation on streaming video.
Starting Page	3210
Ending Page	3217
File Size	693941
Page Count	8
File Format	PDF
ISBN	9781467312264
ISSN	10636919
e-ISBN	9781467312288
e-ISBN	9781467312271
DOI	10.1109/CVPR.2012.6248056
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Hidden Markov models Computational modeling Spatiotemporal phenomena Streaming media Principal component analysis Memory management
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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

Streaming face recognition using multicamera video arrays

Speaker recognition using kernel mutual subspace method

Object Trajectory-Based Activity Classification and Recognition Using Hidden Markov Models

Detection of human actions from a single example

Hierarchical spatio-temporal context modeling for action recognition

Maximum likelihood multiple subspace projections for hidden Markov models

Handwritten Amharic Bank Check Recognition Using Hidden Markov Random Field

Using Principal Component Analysis and Hidden Markov Model for Hand Recognition Systems

PCA feature extraction for change detection in multidimensional unlabelled streaming data

Foreground detection using spatiotemporal projection kernels

Similar Documents

Streaming face recognition using multicamera video arrays

Speaker recognition using kernel mutual subspace method

Object Trajectory-Based Activity Classification and Recognition Using Hidden Markov Models

Detection of human actions from a single example

Hierarchical spatio-temporal context modeling for action recognition

Maximum likelihood multiple subspace projections for hidden Markov models

Handwritten Amharic Bank Check Recognition Using Hidden Markov Random Field

Using Principal Component Analysis and Hidden Markov Model for Hand Recognition Systems

PCA feature extraction for change detection in multidimensional unlabelled streaming data

Foreground detection using spatiotemporal projection kernels