NDLI: Multiple features based shared models for background subtraction

Content Provider	IEEE Xplore Digital Library
Author	Yingying Chen Jinqiao Wang Jianqiang Li Hanqing Lu
Copyright Year	2015
Description	Author affiliation: Nat. Lab. of Pattern Recognition, Inst. of Autom., Beijing, China (Yingying Chen; Jinqiao Wang; Hanqing Lu) \|\| Sch. of Software Eng., Beijing Univ. of Technol., Beijing, China (Jianqiang Li)
Abstract	Background modeling is a fundamental problem in computer vision and usually as the first step for high-level applications. Pixel based approaches usually ignore the spatial coherence, while region based approaches are sensitive to region size and scene complexity. In this paper, we propose a robust background subtraction approach via multiple features based shared models. Each shared model is represented by a sequence of samples based on sample consensus. Each pixel dynamically searches a matched model around the neighborhood. This shared mechanism not only enhances the robustness for background noise and jitter but also significantly reduces the number of models and samples for each model. Besides, we concatenate color and texture features as multiple features according to the discriminability and complementarity, so that each pixel can find a proper model more easily. Finally, the shared models are updated by random selecting a pixel matched the model with an adaptive update rate. Experiments on ChangeDetection benchmark 2014 show that the proposed approach outperforms the state-of-the-art methods.
Starting Page	3946
Ending Page	3950
File Size	1771232
Page Count	5
File Format	PDF
e-ISBN	9781479983391
DOI	10.1109/ICIP.2015.7351545
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Computational modeling Color Robustness Benchmark testing Jitter Noise measurement shared model Background 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

Learning sharable models for robust background subtraction

Multimode spatiotemporal background modeling for complex scenes

Objects Detecting Based on Adaptive Background Models and Multiple Cues

Background subtraction: Experiments and improvements for ViBe

A novel background subtraction method based on color invariants and grayscale levels

Shadow removal using background reconstruction

A Codebook Based Background Subtraction Method for Image Defects Detection

Effective multi-resolution background subtraction

Adaptive texture-color based background subtraction for video surveillance

Multiple features based shared models for background subtraction

Similar Documents

Learning sharable models for robust background subtraction

Multimode spatiotemporal background modeling for complex scenes

Objects Detecting Based on Adaptive Background Models and Multiple Cues

Background subtraction: Experiments and improvements for ViBe

A novel background subtraction method based on color invariants and grayscale levels

Shadow removal using background reconstruction

A Codebook Based Background Subtraction Method for Image Defects Detection

Effective multi-resolution background subtraction

Adaptive texture-color based background subtraction for video surveillance

Multiple features based shared models for background subtraction