ViBe: A Disruptive Method for Background Subtraction

Background subtraction; Model; ViBe; Segmentation; Updating; Motion detection; Real time; Complexity factor; Benchmarking; Complexity analysis; Source code in C; Static b ackground; Exponential filter; Downscaled version of ViBe; Non-parametric model; Random update; Spatial diffusion; Principles

Abstract :

[en] This chapter presents ViBe and the underlying ideas of the algorithm. ViBe is an algorithm for the dection of motion by background subtraction. It is a very fast algorithm, based on samples and several innovative processes (time subsampling, random substitution, spatial diffusion, etc). In addtion, we propose a way to measure the computation time by introducing the notion of complexity factor.

Research center :

Intelsig
Telim
Montefiore

Disciplines :

Electrical & electronics engineering

Author, co-author :

Van Droogenbroeck, Marc ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications

Barnich, Olivier

Language :

English

Title :

ViBe: A Disruptive Method for Background Subtraction

Publication date :

July 2014

Main work title :

Background Modeling and Foreground Detection for Video Surveillance

Editor :

Bouwans

Porikli

Hoferlin

Vacavant

Publisher :

Chapman and Hall/CRC

ISBN/EAN :

9781482205374

Pages :

7.1-7.23

Peer reviewed :

Peer reviewed

Additional URL :

http://www2.ulg.ac.be/telecom/research/vibe
http://www2.ulg.ac.be/telecom/publi/publications/mvd/VanDroogenbroeck2014ViBe/

Commentary :

Please note that we provide the source code in C (optimized sequential version) for educational and research purposes only (apply for the package package vibe-sources.zip on page http://orbi.ulg.ac.be/handle/2268/145853 )

Available on ORBi :

since 15 October 2013

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Bibliography

Barnich, O., van Droogenbroeck, M., ViBe: A powerful random technique to estimate the background in video sequences (2009) IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, pp. 945-948. , April
Barnich, O., van Droogenbroeck, M., ViBe: A universal background subtraction algorithm for video sequences (2011) IEEE Transactions on Image Processing, 20 (6), pp. 1709-1724. , June
Benezeth, Y., Jodoin, P., Emile, B., Laurent, H., Rosenberger, C., Review and evaluation of commonly-implemented background subtraction algorithms (2008) IEEE International Conference on Pattern Recognition, ICPR, pp. 1-4. , December
Bouwmans, T., Recent advanced statistical background modeling for foreground detection-a systematic survey (2011) Recent Patents on Computer Science, 4 (3), pp. 147-176
Bouwmans, T., El Baf, F., Vachon, B., Statistical background modeling for foreground detection: A survey (2010) Handbook of Pattern Recognition and Computer Vision, 4, pp. 181-199. , chapter 3, World Scientific Publishing, January
Brutzer, S., Höferlin, B., Heidemann, G., Evaluation of background subtraction techniques for video surveillance (2011) IEEE International Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1937-1944. , Colorado Spring, USA, June
Cucchiara, R., Grana, C., Piccardi, M., Prati, A., Detecting moving objects, ghosts, and shadows in video streams (2003) IEEE Transactions on Pattern Analysis and Machine Intelligence, 25 (10), pp. 1337-1342. , October
Elgammal, A., Harwood, D., Davis, L., Non-parametric model for background subtraction (2000) European Conference on Computer Vision, ECCV 2000, 1843, pp. 751-767. , of Lecture Notes in Computer Science, London, UK, June
Elhabian, S., El-Sayed, K., Ahmed, S., Moving object detection in spatial domain using background removal techniques-State-of-art (2008) Recent Patents on Computer Science, 1, pp. 32-54. , January
Goyette, N., Jodoin, P.-M., Porikli, F., Konrad, J., Ishwar, P., Changedetection.Net: A new change detection benchmark dataset (2012) Change Detection Workshop, CDW, p. 2012. , Providence, Rhode Island, June
Haritaoglu, I., Harwood, D., Davis, L., W4: Real-time surveillance of people and their activities (2000) IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (8), pp. 809-830. , August
Hofmann, M., Tiefenbacher, P., Rigoll, G., Background segmentation with feedback: The pixel-based adaptive segmenter (2012) Change Detection Workshop, CDW, , Providence, Rhode Island, June 2012
(2011), Scientific Development Branch. i-LIDS User Guide
Jodoin, P.-M., Mignotte, M., Konrad, J., Statistical background subtraction using spatial cues (2007) IEEE Transactions on Circuits and Systems for Video Technology, 17 (12), pp. 1758-1763. , December
Kim, K., Chalidabhongse, T., Harwood, D., Davis, L., Real-time foreground-background segmentation using codebook model (2005) Real-Time Imaging, 11 (3), pp. 172-185. , June
Kryjak, T., Gorgon, M., Real-time implementation of the vibe foreground object segmentation algorithm (2013) Proceedings of the Federated Conference on Computer Science and Information Systems, Fedcsis, pp. 591-596. , Krakow, Poland, September
Lin, H.-H., Liu, T.-L., Chuang, J.-C., Learning a scene background model via classification (2009) IEEE Signal Processing Magazine, 57 (5), pp. 1641-1654. , May
Manzanera, A., Σ-Δ background subtraction and the Zipf law (2007) Progress in Pattern Recognition, Image Analysis and Applications, Volume 4756 of Lecture Notes in Computer Science, pp. 42-51. , Springer, November
Manzanera, A., Local jet feature space framework for image processing and representation (2011) International Conference on Signal Image Technology & Internet-Based Systems, pp. 261-268. , Dijon, France, November
Manzanera, A., Richefeu, J., A new motion detection algorithm based on Σ-Δ background estimation (2007) Pattern Recognition Letters, 28 (3), pp. 320-328. , February
Moeslund, T., Hilton, A., Krüger, V., Sigal, L., (2011) Visual Analysis of Humans: Looking at People, , Springer
Mould, N., Havlicek, J., A conservative scene model update policy Southwest Symposium on Image Analysis and Interpretation, p. 2012. , Santa Fee, New Mexico, USA, April
Mould, N., Havlicek, J., A stochastic learning algorithm for pixel-level background models (2012) IEEE International Conference on Image Processing, ICIP, pp. 1233-1236. , September
Papoulis, A., (1984) Probability, Random Variables, and Stochastic Processes, , McGraw-Hill
Parks, D., Fels, S., Evaluation of background subtraction algorithms with post-processing (2008) IEEE International Confersence on Advanced Video and Signal Based Surveillance, pp. 192-199. , Santa Fe (New Mexico, USA, September
Porikli, F., Tuzel, O., Bayesian background modeling for foreground detection (2005) ACM International Workshop on Video Surveillance & Sensor Networks, pp. 55-58. , Singapore, November
Radke, R., Andra, S., Al-Kofahi, O., Roysam, B., Image change detection algorithms: A systematic survey (2005) IEEE Transactions on Image Processing, 14 (3), pp. 294-307
Schick, A., Bauml, M., Stiefelhagen, R., Improving foreground segmentation with probabistic superpixel Markov random fields (2012) Change Detection Workshop, CDW 2012, , Providence, Rhode Island, June
Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A., Real-time human pose recognition in parts from single depth images (2011) IEEE International Conference on Computer Vision and Pattern Recognition, CVPR, , Colorado Springs, June
Shoushtarian, B., Bez, H., A practical adaptive approach for dynamic background subtraction using an invariant colour model and object tracking (2005) Pattern Recognition Letters, 26 (1), pp. 5-26. , January
Srivastava, A., Lee, A., Simoncelli, E., Zhu, S., On advances in statistical modeling of natural images (2003) Journal of Mathematical Imaging and Vision, 18 (1), pp. 17-33. , January
Stauffer, C., Grimson, E., Adaptive background mixture models for real-time tracking (1999) IEEE International Conference on Computer Vision and Pattern Recognition, CVPR, 2, pp. 246-252. , Ft. Collins, USA, June
Sun, L., de Neyer, Q., de Vleeschouwer, C., Multimode spatiotemporal background modeling for complex scenes (2012) European Signal Processing Conference, EUSIPCO, pp. 165-169. , Bucharest, Romania, August
van Droogenbroeck, M., Barnich, O., (2010) Visual Background Extractor, , European Patent Office, EP 2 015 252 B1, February
van Droogenbroeck, M., Barnich, O., (2011) Visual Background Extractor, , Japan Patent Office, JP 2011 4699564 B2, June
van Droogenbroeck, M., Barnich, O., (2011), p. 18. , Visual background extractor. United States Patent and Trademark Office, US 8,009,918 B2, August
van Droogenbroeck, M., Paquot, O., Background subtraction: Experiments and improvements for ViBe (2012) Change Detection Workshop, CDW 2012, pp. 32-37. , Providence, Rhode Island, June
Wang, H., Suter, D., A consensus-based method for tracking: Modelling background scenario and foreground appearance (2007) Pattern Recognition, 40 (3), pp. 1091-1105
Wren, C., Azarbayejani, A., Darrell, T., Pentland, A., Pfinder: Real-time tracking of the human body (1997) IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (7), pp. 780-785. , July
Zivkovic, Z., van Der Heijden, F., Efficient adaptive density estimation per image pixel for the task of background subtraction (2006) Pattern Recognition Letters, 27 (7), pp. 773-780. , May