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ARTOD: Autonomous Real Time Objects Detection by a moving camera using recursive density estimation

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

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Standard

ARTOD: Autonomous Real Time Objects Detection by a moving camera using recursive density estimation. / Sadeghi Tehran, Pouria; Angelov, Plamen.
Novel applications of intelligent systems. ed. / Mincho Hadjiski; Nikola Kasabov; Dimitar Filev; Vladimir Jotsov. Cham: Springer Verlag, 2016. p. 123-138 (Studies in Computational Intelligence; Vol. 586).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Harvard

Sadeghi Tehran, P & Angelov, P 2016, ARTOD: Autonomous Real Time Objects Detection by a moving camera using recursive density estimation. in M Hadjiski, N Kasabov, D Filev & V Jotsov (eds), Novel applications of intelligent systems. Studies in Computational Intelligence, vol. 586, Springer Verlag, Cham, pp. 123-138. https://doi.org/10.1007/978-3-319-14194-7_7

APA

Sadeghi Tehran, P., & Angelov, P. (2016). ARTOD: Autonomous Real Time Objects Detection by a moving camera using recursive density estimation. In M. Hadjiski, N. Kasabov, D. Filev, & V. Jotsov (Eds.), Novel applications of intelligent systems (pp. 123-138). (Studies in Computational Intelligence; Vol. 586). Springer Verlag. https://doi.org/10.1007/978-3-319-14194-7_7

Vancouver

Sadeghi Tehran P, Angelov P. ARTOD: Autonomous Real Time Objects Detection by a moving camera using recursive density estimation. In Hadjiski M, Kasabov N, Filev D, Jotsov V, editors, Novel applications of intelligent systems. Cham: Springer Verlag. 2016. p. 123-138. (Studies in Computational Intelligence). doi: 10.1007/978-3-319-14194-7_7

Author

Sadeghi Tehran, Pouria ; Angelov, Plamen. / ARTOD : Autonomous Real Time Objects Detection by a moving camera using recursive density estimation. Novel applications of intelligent systems. editor / Mincho Hadjiski ; Nikola Kasabov ; Dimitar Filev ; Vladimir Jotsov. Cham : Springer Verlag, 2016. pp. 123-138 (Studies in Computational Intelligence).

Bibtex

@inbook{55b4aa4b3c4e4552a7d4a2252b335e6f,
title = "ARTOD: Autonomous Real Time Objects Detection by a moving camera using recursive density estimation",
abstract = "A new approach to autonomously detect moving objects in a video captured by amoving camera is proposed in this chapter. The proposed method is separated in two modules.In the first part, the well-known scale invariant feature transformation (SIFT) and the RANSAC algorithm are used to estimate the camera movement. In the second part, recursive density estimation (RDE) is used to build a model of the background and detect moving objects in a scene. The results are presented for both indoor and outdoor video sequences taken from a UAV for outdoor scenario and handheld camera for indoor experiment.",
keywords = "image processing, intelligent video analytics",
author = "{Sadeghi Tehran}, Pouria and Plamen Angelov",
year = "2016",
month = jan,
day = "28",
doi = "10.1007/978-3-319-14194-7_7",
language = "English",
isbn = "9783319141930",
series = "Studies in Computational Intelligence",
publisher = "Springer Verlag",
pages = "123--138",
editor = "Mincho Hadjiski and Nikola Kasabov and Dimitar Filev and Vladimir Jotsov",
booktitle = "Novel applications of intelligent systems",

}

RIS

TY - CHAP

T1 - ARTOD

T2 - Autonomous Real Time Objects Detection by a moving camera using recursive density estimation

AU - Sadeghi Tehran, Pouria

AU - Angelov, Plamen

PY - 2016/1/28

Y1 - 2016/1/28

N2 - A new approach to autonomously detect moving objects in a video captured by amoving camera is proposed in this chapter. The proposed method is separated in two modules.In the first part, the well-known scale invariant feature transformation (SIFT) and the RANSAC algorithm are used to estimate the camera movement. In the second part, recursive density estimation (RDE) is used to build a model of the background and detect moving objects in a scene. The results are presented for both indoor and outdoor video sequences taken from a UAV for outdoor scenario and handheld camera for indoor experiment.

AB - A new approach to autonomously detect moving objects in a video captured by amoving camera is proposed in this chapter. The proposed method is separated in two modules.In the first part, the well-known scale invariant feature transformation (SIFT) and the RANSAC algorithm are used to estimate the camera movement. In the second part, recursive density estimation (RDE) is used to build a model of the background and detect moving objects in a scene. The results are presented for both indoor and outdoor video sequences taken from a UAV for outdoor scenario and handheld camera for indoor experiment.

KW - image processing

KW - intelligent video analytics

U2 - 10.1007/978-3-319-14194-7_7

DO - 10.1007/978-3-319-14194-7_7

M3 - Chapter

SN - 9783319141930

T3 - Studies in Computational Intelligence

SP - 123

EP - 138

BT - Novel applications of intelligent systems

A2 - Hadjiski, Mincho

A2 - Kasabov, Nikola

A2 - Filev, Dimitar

A2 - Jotsov, Vladimir

PB - Springer Verlag

CY - Cham

ER -