nach oben

The Journal of Supercomputing

Erschienen in:

16.07.2018

Comparative study of illumination-invariant foreground detection

verfasst von: P. R. Karthikeyan, P. Sakthivel, T. S. Karthik

Erschienen in: The Journal of Supercomputing | Ausgabe 4/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Foreground detection plays a vital role in finding the moving objects of a scene. For the last two decades, many methods were introduced to tackle the issue of illumination variation in foreground detection. In this article, we proposed a method to segment moving objects under abrupt illumination change and analyzed the merits and demerits of the proposed method with seven other algorithms commonly used for illumination-invariant foreground detection. The proposed method calculates the entropy of the video scene to determine the level of illumination change occurred and select the update model based on the difference in entropy values. Benchmark datasets possessing different challenging illumination conditions are used to analyze the efficiency of the foreground detection algorithms. Experimental studies demonstrate the performance of the proposed algorithm with several algorithms under various illumination conditions and its low time complexity.

Vorheriger Artikel Performance analysis of data compression algorithms for heterogeneous architecture through parallel approach

Nächster Artikel Internet-of-Things-aided energy management in smart grid environment

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Piccardi M (2004) Background subtraction techniques: a review. IEEE Int Conf Syst Man Cybern 4:3099–3104

Benezeth Y, Jodoin Pierre-Marc, Emile Bruno, Laurent Helene, Rosenberger Christophe (2012) Comparative study of background subtraction algorithms. J Electron Imaging 19(3):12

Ahmed Sumaya H, El-Sayed Khaled M, Elhabian Shireen Y (2008) Moving object detection in spatial domain using background removal techniques-state-of-art. Recent Pat Comput Sci 1(1):32–54CrossRef

Bouwmans Thierry (2014) Traditional and recent approaches in background modeling for foreground detection: an overview. Comput Sci Rev 11(12):31–66CrossRef

Sen-ching SC, Chandrika K (2004) Robust techniques for background subtraction in urban traffic video. In: Proceedings of the SPIE 5308, Visual Communications and Image Processing

Li L, Huang W, Irene YH Gu, Qi Tian (2003, November) Foreground object detection from videos containing complex background. ACM International Conference on Multimedia, pp. 2–10

Wahyono A, Filonenko Jo KH (2016) Unattended object identification for intelligent surveillance systems using sequence of dual background difference. IEEE Trans Ind Inf 12(6):2247–2255CrossRef

Wang K, Liu Y, Gou C, Wang FY (2016) A multi-view learning approach to foreground detection for traffic surveillance applications. IEEE Trans Veh Technol 65(6):4144–4158CrossRef

Wren CR, Azarbayejani A, Darrell T, Pentland AP (1997) Pfinder: real-time tracking of the human body. IEEE Trans Pattern Anal Mach Intell 19(7):780–785CrossRef

10.

McFarlane NJB, Schofield CP (1995) Segmentation and tracking of piglets in images. Mach Vis Appl 8(3):187–193CrossRef

11.

Friedman N, Russell S (1997, August) Image segmentation in video sequences: a probabilistic approach. In: Thirteenth Conference on Uncertainty in Artificial Intelligence, pp. 175–181

12.

Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time tracking. IEEE Comput Soc Conf Comput Vis Pattern Recogn 2:252

13.

Cheng FC, Huang SC, Ruan SJ (2011) Illumination-sensitive background modeling approach for accurate moving object detection. IEEE Trans Broadcast 57(4):794–801CrossRef

14.

Barnich O, Van Droogenbroeck M (2011) ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans Image Process 20(6):1709–1724MathSciNetCrossRef

15.

Manzanera A, Richefeu JC (2004, December) A robust and computationally efficient motion detection algorithm based on ∑-Δ background estimation. In: Indian Conference on Computer Vision, Graphics and Image Processing, pp. 46–51

16.

Lou J, Yang H, Hu W, Tan T (2002, January) An illumination invariant change detection algorithm. In: Asian Conference on Computer Vision, pp. 13–18

17.

Holtzhausen PJ, Crnojevic V, Herbst BM (2015) An illumination invariant framework for real-time foreground detection. J Real Time Image Process 10(2):423–433CrossRef

18.

Elharrouss O, Abbad A, Moujahid D, Tairi H (2018) Moving object detection zone using a block-based background model. IET Comput Vis 12(1):86–94CrossRef

19.

Kim W, Kim Y (2016) Background subtraction using illumination-invariant structural complexity. IEEE Signal Process Lett 23(5):634–638CrossRef

20.

Toyama K, Krumm J, Brumitt B, Meyers B (1999) Wallflower: principles and practice of background maintenance. IEEE Int Conf Comput Vis 1:255–261

21.

Li Liyuan, Huang Weimin, Irene Yu-Hua Gu, Tian Qi (2004) Statistical modeling of complex backgrounds for foreground object detection. IEEE Trans Image Process 13(11):1459–1472CrossRef

Titel: Comparative study of illumination-invariant foreground detection
verfasst von: P. R. Karthikeyan
P. Sakthivel
T. S. Karthik
Publikationsdatum: 16.07.2018
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 4/2020
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-018-2488-1

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 4/2020

Structured mesh-oriented framework design and optimization for a coarse-grained parallel CFD solver based on hybrid MPI/OpenMP programming

Rio: a personal storage system in multi-device and cloud

Efficient computation offloading for Internet of Vehicles in edge computing-assisted 5G networks

Correction to: Efficient noise injection for exposing hidden data races

A novel warp scheduling scheme considering long-latency operations for high-performance GPUs

A new algorithm of stock data mining in Internet of Multimedia Things