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An approach to automatic real-time novelty detection, object identification, and tracking in video streams based on recursive density estimation and evolving Takagi–Sugeno fuzzy systems

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An approach to automatic real-time novelty detection, object identification, and tracking in video streams based on recursive density estimation and evolving Takagi–Sugeno fuzzy systems. / Angelov, Plamen; Sadeghi-Tehran, Pouria; Ramezani, Ramin.

In: International Journal of Intelligent Systems, Vol. 26, No. 3, 03.2011, p. 189-205.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Angelov P, Sadeghi-Tehran P, Ramezani R. An approach to automatic real-time novelty detection, object identification, and tracking in video streams based on recursive density estimation and evolving Takagi–Sugeno fuzzy systems. International Journal of Intelligent Systems. 2011 Mar;26(3):189-205. Epub 2010 Dec 7. doi: 10.1002/int.20462

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@article{521376f6b5fc413792cd3bd81b54ced8,
title = "An approach to automatic real-time novelty detection, object identification, and tracking in video streams based on recursive density estimation and evolving Takagi–Sugeno fuzzy systems",
abstract = "Recently, surveillance, security, patrol, search, and rescue applications increasingly require algorithms and methods that can work automatically in real time. This paper reports a new real-time approach based on three novel techniques for automatic detection, object identification, and tracking in video streams, respectively. The novelty detection and object identification are based on the newly proposed recursive density estimation (RDE) method. RDE is using a Cauchy-type of kernel, which is calculated recursively as opposed to the widely used (in particular in the kernel density estimation (KDE) approach) Gaussian one. The key difference is that the proposed approach works on a per frame basis and does not require a window (usually of size of several dozen) of frames to be stored in the memory and processed. It should be noted that the new RDE approach is free from user- or problem-specific thresholds by differ from the other state-of-the-art approaches. Finally, an evolving Takagi-Sugeno (eTS)-type fuzzy system is proposed for tracking. The proposed approach has been compared with KDE and Kalman filter (KF) and has proven to be significantly (in an order of magnitude) faster and computationally more efficient than RDE and more precise than KF. ",
author = "Plamen Angelov and Pouria Sadeghi-Tehran and Ramin Ramezani",
year = "2011",
month = mar,
doi = "10.1002/int.20462",
language = "English",
volume = "26",
pages = "189--205",
journal = "International Journal of Intelligent Systems",
issn = "0884-8173",
publisher = "John Wiley and Sons Ltd",
number = "3",

}

RIS

TY - JOUR

T1 - An approach to automatic real-time novelty detection, object identification, and tracking in video streams based on recursive density estimation and evolving Takagi–Sugeno fuzzy systems

AU - Angelov, Plamen

AU - Sadeghi-Tehran, Pouria

AU - Ramezani, Ramin

PY - 2011/3

Y1 - 2011/3

N2 - Recently, surveillance, security, patrol, search, and rescue applications increasingly require algorithms and methods that can work automatically in real time. This paper reports a new real-time approach based on three novel techniques for automatic detection, object identification, and tracking in video streams, respectively. The novelty detection and object identification are based on the newly proposed recursive density estimation (RDE) method. RDE is using a Cauchy-type of kernel, which is calculated recursively as opposed to the widely used (in particular in the kernel density estimation (KDE) approach) Gaussian one. The key difference is that the proposed approach works on a per frame basis and does not require a window (usually of size of several dozen) of frames to be stored in the memory and processed. It should be noted that the new RDE approach is free from user- or problem-specific thresholds by differ from the other state-of-the-art approaches. Finally, an evolving Takagi-Sugeno (eTS)-type fuzzy system is proposed for tracking. The proposed approach has been compared with KDE and Kalman filter (KF) and has proven to be significantly (in an order of magnitude) faster and computationally more efficient than RDE and more precise than KF. 

AB - Recently, surveillance, security, patrol, search, and rescue applications increasingly require algorithms and methods that can work automatically in real time. This paper reports a new real-time approach based on three novel techniques for automatic detection, object identification, and tracking in video streams, respectively. The novelty detection and object identification are based on the newly proposed recursive density estimation (RDE) method. RDE is using a Cauchy-type of kernel, which is calculated recursively as opposed to the widely used (in particular in the kernel density estimation (KDE) approach) Gaussian one. The key difference is that the proposed approach works on a per frame basis and does not require a window (usually of size of several dozen) of frames to be stored in the memory and processed. It should be noted that the new RDE approach is free from user- or problem-specific thresholds by differ from the other state-of-the-art approaches. Finally, an evolving Takagi-Sugeno (eTS)-type fuzzy system is proposed for tracking. The proposed approach has been compared with KDE and Kalman filter (KF) and has proven to be significantly (in an order of magnitude) faster and computationally more efficient than RDE and more precise than KF. 

U2 - 10.1002/int.20462

DO - 10.1002/int.20462

M3 - Journal article

VL - 26

SP - 189

EP - 205

JO - International Journal of Intelligent Systems

JF - International Journal of Intelligent Systems

SN - 0884-8173

IS - 3

ER -