NDLI: Autonomous novelty detection and object tracking in video streams using evolving clustering and Takagi-Sugeno type neuro-fuzzy system

Content Provider	IEEE Xplore Digital Library
Author	Angelov, P. Ramezani, R. Xiaowei Zhou
Copyright Year	2008
Description	Author affiliation: Digital Signal Process. Group, Lancaster Univ., Lancaster (Angelov, P.; Ramezani, R.; Xiaowei Zhou)
Abstract	Autonomous systems for surveillance, security, patrol, search and rescue are the focal point of extensive research and interest from defense and the security related industry, traffic control and other institutions. A range of sensors can be used to detect and track objects, but optical cameras or camcorders are often considered due to their convenience and passive nature. Tracking based on color intensity information is often preferred than the motion cues due to being more robust. The technique presented in this paper can also be used in conjunction with infra-red cameras, 3D lasers which result in a grey scale image. Novelty detection and tracking are two of the key elements of such systems. Most of the currently reported techniques are characterized by high computational, memory storage costs and are not autonomous because they usually require a human operator in the loop. This paper presents new approaches to both the problem of novelty detection and object tracking in video streams. These approaches are rooted in the recursive techniques that are computationally efficient and therefore potentially applicable in real-time. A novel approach for recursive density estimation (RDE) using a Cauchy type of kernel (as opposed to the usually used Gaussian one) is proposed for visual novelty detection and the use of the recently introduced evolving Takagi-Sugeno (eTS) neuro-fuzzy system for tracking the object detected by the RDE approach is proposed as opposed to the usually used Kalman filter (KF). In fact, eTS can be seen as a fuzzily weighted mixture of KF. The proposed technique is significantly faster than the well known kernel density estimation (KDE) approach for background subtraction for novelty detection and is more precise than the usually used KF. Additionally the overall approach removes the need of manually selecting the object to be tracked which makes possible a fully autonomous system for novelty detection and tracking to be developed.
Starting Page	1456
Ending Page	1463
File Size	320018
Page Count	8
File Format	PDF
ISBN	9781424418206
ISSN	10987576
DOI	10.1109/IJCNN.2008.4633989
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pixel Image color analysis Streaming media Kernel Probability density function Estimation Security
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

Improving Premise Structure in Evolving Takagi-Sugeno Neuro-Fuzzy Classifiers

A fast approach to novelty detection in video streams using recursive density estimation

RTSDE: Recursive total-sum-distances-based density estimation approach and its application for autonomous real-time video analytics

Neuro-fuzzy based real time object tracking.

A Novel Trust Region Tracking Algorithm Based on Kernel Density Estimation

Real-time novelty detection in video using background subtraction techniques: State of the art a practical review

DEC: Dynamically Evolving Clustering and Its Application to Structure Identification of Evolving Fuzzy Models

Eye detection and tracking in video streams

Mean shift tracking using fuzzy color histogram

Autonomous novelty detection and object tracking in video streams using evolving clustering and Takagi-Sugeno type neuro-fuzzy system

Similar Documents

Improving Premise Structure in Evolving Takagi-Sugeno Neuro-Fuzzy Classifiers

A fast approach to novelty detection in video streams using recursive density estimation

RTSDE: Recursive total-sum-distances-based density estimation approach and its application for autonomous real-time video analytics

Neuro-fuzzy based real time object tracking.

A Novel Trust Region Tracking Algorithm Based on Kernel Density Estimation

Real-time novelty detection in video using background subtraction techniques: State of the art a practical review

DEC: Dynamically Evolving Clustering and Its Application to Structure Identification of Evolving Fuzzy Models

Eye detection and tracking in video streams

Mean shift tracking using fuzzy color histogram

Autonomous novelty detection and object tracking in video streams using evolving clustering and Takagi-Sugeno type neuro-fuzzy system