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

Angelov, Plamen and Ramezani, Ramin and Zhou, Xiao (2008) Autonomous novelty detection and object tracking in video streams using evolving clustering and Takagi-Sugeno type neuro-fuzzy system. In: IEEE International Joint Conference on Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE, Hong Kong, pp. 1456-1463. ISBN 978-1-4244-1820-6

Full text not available from this repository.


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. (c) IEEE Press

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
"©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."
Uncontrolled Keywords:
ID Code:
Deposited By:
Deposited On:
01 Oct 2009 09:03
Last Modified:
22 Sep 2023 01:01