Skip to main content
SpringerLink
Log in

Fast background subtraction using static and dynamic gates

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Background subtraction is usually one of the first steps carried out in motion detection using static video cameras. This paper presents a new fast model for background subtraction that processes only some pixels of each image. This model achieves a significant reduction in computation time that can be used for subsequent image analysis. Some regions of interest (ROI) are located where movement can start. If no movement is present in the image, only pixels of these ROIs are processed. Once a moving object is detected, a new ROI that follows it is created. Thus, motion detection and parameter updates are executed only in the relevant areas instead of in the whole image. The proposed model has three main advantages: the computational time can be reduced drastically, motion detection performance is improved, and it can be combined with most of the existing background subtraction techniques. These features make it specially suitable for security applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Beleznai C, Sommer P, Bischof H (2007) Scale-adaptive clustering for object detecion and counting. In: IEEE international workshop on PETS, IEEE, IEEE pp 9–16

  • Brown L., Senior A., Tian Y., Connell J., Hampapur A., Shu C., Merkl H., Lu M. (2005) Performance evaluation of surveillance systems under varying conditions. In: IEEE international workshop on performance evaluation of tracking and surveillance, Citeseer, pp. 79–87.

  • Cermak G, Keyzer K (2007) Unsupervised intrusion detection using color images. In: ISVC (2), pp 770–780

  • Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. Pattern Anal Mach Intell IEEE Trans 24(5): 603–619

    Article  Google Scholar 

  • Comaniciu D, Ramesh V, Meer P (2001) The variable bandwidth mean shift and data-driven scale selection. In: Computer vision, 2001. ICCV 2001. Proceedings of the eighth IEEE international conference on, vol 1, pp 438 –445

  • Del Rose M, Wagner C (2011) Survey on classifying human actions through visual sensors. Artif Intell Rev:1–11. doi:10.1007/s10462-011-9232-z

  • Dockstader S, Tekalp A (2000). Real-time object tracking and human face detection in cluttered scenes. In: image and video communications and processing, 2000. Proceedings of the society of photo-optical instrumentation engineers (SPIE), vol 3974, pp 957–968

  • Elgammal A, Harwood D, Davis L (2000) Non-parametric model for background subtraction. Lect Notes Comput Sci 1843: 751–767

    Article  Google Scholar 

  • Hampapur A, Brown L, Connell J, Pankanti S, Senior A, Tian Y (2003) Smart surveillance: applications, technologies and implications. In: Information, communications and signal processing, 2003 and the fourth pacific rim conference on multimedia. Proceedings of the 2003 joint conference of the fourth international conference on, vol 2, pp 1133–1138

  • Howarth R (2005) Spatial models for wide-area visual surveillance: computational approaches and spatial building-blocks. Artif Intell Rev 23: 97–155. doi:10.1007/s10462-004-4103-5

    Article  Google Scholar 

  • Kim B-G, Park D-J (2006) Novel target segmentation and tracking based on fuzzy membership distribution for vision-based target tracking system. Image Video Comput 24(12): 1319–1331

    Article  Google Scholar 

  • Ko T (2008) A survey on behavior analysis in video surveillance for homeland security applications. In: Applied imagery pattern recognition workshop, 2008. AIPR ’08, 37th IEEE, pp 1–8

  • Lee D-S (2005) Effective gaussian mixture learning for video background subtraction. Pattern Anal Mach Intell IEEE Trans 27(5): 827–832

    Article  Google Scholar 

  • Li H, Wang J (1993) Computing optical flow with a recurrent neural network. J Pattern Anal Artif Intell 7: 801–814

    Article  Google Scholar 

  • Li L, Huang W, Gu IY-H, Tian Q (2004) Statistical modeling of complex backgrounds for foreground object detection. Image Process IEEE Trans 13(11): 1459–1472

    Article  Google Scholar 

  • Liu X, Tu P, Rittscher J, Perera A, Krahnstoever N (2005) Detecting and counting people in surveillance applications. In: Advanced video and signal based surveillance, 2005. AVSS 2005. IEEE conference on, pp 306–311

  • Maddalena L, Petrosino A (2008) A self-organizing approach to background subtraction for visual surveillance applications. Image Process IEEE Trans 17(7): 1168–1177

    Article  MathSciNet  Google Scholar 

  • Munkelt O, Kirchner H (1996) STABIL: a system for monitoring persons in image sequences. In: Image and video processing IV, 1996. Proceedings of the society of photo-optical instrumentation engineers (SPIE), vol 2666, pp 163–179

  • Piccardi M (2004) Background subtraction techniques: a review. In: Systems, man and cybernetics, 2004 IEEE international conference on, vol 4, pp 3099–3104

  • Piccardi M, Jan T (2004) Mean-shift background image modelling. In Image processing, 2004. ICIP ’04. 2004 international conference on, vol 5, pp 3399–3402

  • Poppe C, Martens G, Lambert P, Vande Walle R (2007) Improved background mixture models for video surveillance applications. Lect Notes Comput Sci 4843: 251

    Article  Google Scholar 

  • Power P, Schoonees J (2002) Understanding background mixture models for foreground segmentation. In: Proceedings image and vision computing new Zealand, pp 267–271

  • Stauffer C, Grimson W (1999) Adaptive background mixture models for real-time tracking. In: Computer vision and pattern recognition, 1999. IEEE computer society conference on, vol 2, pp 246–252

  • Tao H, Sawhney H, Kumar R (2002) Object tracking with bayesian estimation of dynamic layer representations. Pattern Anal Mach Intell IEEE Trans 24(1): 75–89

    Article  Google Scholar 

  • Tsai S-C, Lai D-M (2009) Independent component analysis-based background subtraction for indoor surveillance. Image Process IEEE Trans 18(1): 158–167

    Article  MathSciNet  Google Scholar 

  • Wren C, Azarbayejani A, Darrell T, Pentland A (1996) Pfinder: Real-time tracking of the human body. In: Tescher AG, Bove VM (eds) Intefration issues in large commercial media delivery systems, 1996. Proceedings of the society of photo-optical instrumentation engineers (SPIE), vol 2615, pp 89–98

  • Wu B, Nevatia R (2007) Detection and tracking of multiple, partially occluded humans by Bayesian combination of edgelet based part detectors. Int J Comput Vis 75(2): 247–266

    Article  Google Scholar 

  • Wu J, Trivedi M (2005) Performance characterization for gaussian mixture model based motion detection algorithms. In: Image processing, 2005. ICIP 2005. IEEE international conference on, vol 1, pp 1097–100

  • Zhan C, Duan X, Xu S, Song Z, Luo M (2007) An improved moving object detection algorithm based on frame difference and edge detection. In: Image and graphics, 2007. ICIG 2007. Fourth international conference on, pp 519–523

  • Zhang S, Yao H, Liu S (2009) Dynamic background subtraction based on local dependency histogram. Int J Pattern Recognit Artif Intell 23(7): 1397–1419

    Article  Google Scholar 

  • Zhao T, Nevatia R (2004) Tracking multiple humans in complex situations. Pattern Anal Mach Intell IEEE Trans 26(9): 1208–1221

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Research in Technology, Advanced Technical Faculty of Engineering, Comillas Pontifical University, Alberto, Aguilera 23, 28015, Madrid, Spain

    Manuel Alvar, Álvaro Sánchez & Álvaro Arranz

Authors
  1. Manuel Alvar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Álvaro Sánchez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Álvaro Arranz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manuel Alvar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Alvar, M., Sánchez, Á. & Arranz, Á. Fast background subtraction using static and dynamic gates. Artif Intell Rev 41, 113–128 (2014). https://doi.org/10.1007/s10462-011-9301-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-011-9301-3

Keywords

Search

Navigation