nach oben

Erschienen in:

2019 | OriginalPaper | Buchkapitel

Detection of Moving Objects in Video Using Block-Based Approach

verfasst von : Amlan Raychaudhuri, Satyabrata Maity, Amlan Chakrabarti, Debotosh Bhattacharjee

Erschienen in: Advances in Intelligent Computing

Verlag: Springer Singapore

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

In this paper, an efficient technique has been proposed to detect moving objects in the video under dynamic as well as static background condition. The proposed method consists block-based background modelling, current frame updating, block processing of updated current frame and elimination of background using bin histogram approach. Next, enhanced foreground objects are obtained in the post-processing stage using morphological operations. The proposed approach effectively minimizes the effect of dynamic background to extract the foreground information. We have applied our proposed technique on Change Detection CDW-2012 dataset and compared the results with the other state-of-the-art methods. The experimental results prove the efficiency of the proposed approach compared to the other state-of-the-art methods in terms of different evaluation metrics.

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

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

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"

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

Vorheriges Kapitel A Column-Wise Distance-Based Approach for Clustering of Gene Expression Data with Detection of Functionally Inactive Genes and Noise

Bovik, A.C.: Hand Book on Image and Video Processing. Academic Press, New York (2000)MATH

Tekalp, A.M.: Digital Video Processing. Prentice Hall, New Jersey (1995)

Shaikh, S.H., Saeed, K., Chaki, N.: Moving object detection using background subtraction. In: Springer Briefs in Computer Science (2014)

Piccardi, M.: Background subtraction techniques: a review. In: Proceedings IEEE International Conference on Systems, Man and Cybernetics (SMC), vol. 4, pp. 3099–3104 (2004)

Elhabian, S., El-Sayed, K., Ahmed, S.: Moving object detection in spatial domain using background removal techniques — state-of-art. Recent Patents Comput. Sci. 1, 32–54 (2008)CrossRef

Fejes, S., Davis, L.S.: Detection of independent motion using directional motion estimation. Comput. Vis. Image Understand. (CVIU) 74(2), 101–120 (1999)CrossRef

Paragios, N., Deriche, R.: Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 22(3), 266–280 (2000)CrossRef

McIvor, A.M.: Background subtraction techniques. In: Proceedings of Image and Vision Computing (2000)

Heikkila, J., Silven, O.: A real-time system for monitoring of cyclists and pedestrians. Proc. Image Vis. Comput. 22(7), 563–570 (2004)CrossRef

10.

Lipton, A.J., Fujiyoshi, H., Patil, R.S.: Moving target classification and tracking from real time video. In: Proceedings 4th IEEE Workshop on Applications of Computer Vision (WACV ’98), pp. 129–136 (1998)

11.

Wang, L., Hu, W., Tan, T.: Recent developments in human motion analysis. Pattern Recogn. 36(3), 585–601 (2003)CrossRef

12.

Collins, R.T., Lipton, A.J., Kanade, T., Fujiyoshi, H., Duggins, D., Tsin, Y., Tolliver, D., Enomoto, N., Hasegawa, O., Burt, P., Wixson, L.: A system for video surveillance and monitoring. In: Technical Report CMU-RI-TR-00-12. The Robotics Institute, Carnegie Mellon University (2000)

13.

Haritaoglu, I., Harwood, D., Davis, L.S.: W⁴: Real-time surveillance of people and their activities. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 809–830 (2000)CrossRef

14.

Kumar, S., Yadav, J.S.: Video object extraction and its tracking using background subtraction in complex environments. Perspect. Sci. 8, 317–322 (2016)CrossRef

15.

Rahman, A.Y.F., Hussain, A., Zaki, W., Zaman, B., Tahir, M.: Enhancement of background subtraction techniques using a second derivative in gradient direction filter. J. Electr. Comput. Eng. 2013, 1–12 (2013)CrossRef

16.

Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2 (1999)

17.

Heras Evangelio, R., Pätzold, M., Sikora, T.: Splitting Gaussians in mixture models. In: Proceedings of the 9th IEEE International Conference on Advanced Video and Signal-Based Surveillance (2012)

18.

Varadarajan, S., Miller, P., Huiyu Z.: Spatial mixture of Gaussians for dynamic background modeling. In: Proceedings of the 10th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS ’13), pp. 63–68 (2013)

19.

Tiefenbacher, P., Hofmann, M., Merget, D., Rigoll, G.: PID-based regulation of background dynamics for foreground segmentation. In: Proceedings of Image Processing (ICIP), pp. 3282–3286 (2014)

20.

Morde, A., Ma, X., Guler, S.: Learning a background model for change detection. In: Proceedings of IEEE Workshop on Change Detection (2012)

21.

Wixson, L.: Detecting salient motion by accumulating directionally—consistent flow. IEEE Trans. Pattern Anal. Mach. Intell. 22(8) (2000)CrossRef

22.

Pless, R., Brodsky, T., Aloimonos, Y.: Detecting independent motion: the statistics of temporal continuity. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 768–773 (2000)CrossRef

23.

Raychaudhuri, A., Maity, S., Chakrabarti, A., Bhattacharjee, D.: Moving object detection in video under dynamic background condition using block-based statistical features. In: Proceedings CICBA 2017, Part II, CCIS 776, pp. 371–383 (2017)

24.

Goyette, N., Jodoin, P.-M., Porikli, F., Konrad, J., Ishwar, P.: Change detection.net: a new change detection benchmark dataset. In: Proceedings IEEE Workshop on Change Detection (CDW-2012) at CVPR (2012)

25.

Hofmann, M., Tiefenbacher, P., Rigoll, G.: Background segmentation with feedback: the pixel-based adaptive segmenter. In: Proceedings IEEE Workshop on Change Detection, pp. 38–43 (2012)

26.

Francisco, J., Lopez, H., Rivera, M.: Change detection by probabilistic segmentation from monocular view. Proc. Mach. Vis. Appl. 25(5), 1175–1195 (2014)CrossRef

Titel: Detection of Moving Objects in Video Using Block-Based Approach
verfasst von: Amlan Raychaudhuri
Satyabrata Maity
Amlan Chakrabarti
Debotosh Bhattacharjee
Verlag: Springer Singapore
Buch: Advances in Intelligent Computing
Print ISBN: 978-981-10-8973-2

Electronic ISBN: 978-981-10-8974-9

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-981-10-8974-9_8

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+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"