Skip to main content
SpringerLink
Log in

An Adaptive Unsupervised Neural Network Based on Perceptual Mechanism for Dynamic Object Detection in Videos with Real Scenarios

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

The analysis of moving objects in video sequences has been a paramount issue in applications related to intelligent surveillance systems, robotics, and medicine. Although several works aimed to analyze objects in video sequences have been reported, many of them need manual parameter adjustments and they are not tolerant to illumination changes and dynamic backgrounds. Therefore, a novel scheme termed Dynamic Retinotopic SOM based on an adaptive artificial neural network, to detect moving objects is proposed in this work. The neural network is a model based on the mechanisms of the visual cortex that we called Retinotopic SOM (RESOM) and it is also proposed in this paper. Furthermore, RESOM is a real-time neural network that can adapt its learning parameters based on the scene behavior and it mimics perception abilities. A quantitative comparison with other segmentation methods reported in the literature using real video scenes showed that the proposed DR-SOM segmentation method automatically adjusts its parameters and outperforms the reported methods in condition of dynamic backgrounds, and gradual and sudden illumination changes.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Hao JY, Li C, Kim Z, Xiong Z (2013) Spatio-temporal traffic scene modeling for object motion detection. IEEE Trans Intell Transp Syst 14:295–302. doi:10.1109/TITS.2012.2212432

    Article  Google Scholar 

  2. Chacon-Murguia MI, Gonzalez-Duarte S (2012) An adaptive neural-fuzzy approach for object detection in dynamic backgrounds for surveillance systems. IEEE Trans Ind Electron 59:3286–3298. doi:10.1109/TIE.2011.2106093

    Article  Google Scholar 

  3. Dotu I, Patricio MA, Molina J (2011) Boosting video tracking performance by means of Tabu Search in intelligent visual surveillance systems. Springer J Heuristics 17(415):440. doi:10.1007/s10732-010-9140-4

    Google Scholar 

  4. Ravier D (2010) A service oriented framework architecture for intelligent video surveillance systems. In: Fifth international conference on digital telecommunications, pp 123–127. doi:10.1109/ICDT.2010.30

  5. Ko T (2008) A survey on behavior analysis in video surveillance for homeland security applications. In: Workshop applied imagery pattern recognition, pp 1–8. doi:10.1109/AIPR.2008.4906450

  6. Reddy V, Sanderson C, Lovell BC, Bigdeli A (2009) An efficient background estimation algorithm for embedded smart cameras. In:Third international conference on distributed smart cameras, pp 1–7. 10.1109/ICDSC.2009.5289348

  7. Cottini N, Gottardi M, Massari N, Passerone R, Smilansky Z (2013) A 33 \(\mu \)W \(64\times 64\) pixel vision sensor embedding robust dynamic background subtraction for event detection and scene interpretation. IEEE J Solid-State Circuits 48:850–863. doi: 10.1109/JSSC.2012.2235031

    Article  Google Scholar 

  8. Chellappa R, Cavallaro A, Wu Y, Shan C, Fu Y, Pulli K (2011) Special issue on video analysis on resource-limited systems. IEEE Trans Circuits Syst Video Technol 21:1349–1352. doi:10.1109/TCSVT.2010.2084830

    Article  Google Scholar 

  9. Lin W-S, Liu A-T, Fang C-H (2005) Computational autonomous visual perception using cellular neural networks. In: IEEE international conference on computational intelligence for measurement systems and applications, pp 198–202. doi:10.1109/CIMSA.2005.1522860

  10. Yu B, Zahng L (2004) Pulse-coupled neural networks for contour and motion matchings. IEEE Trans Neural Networks 15:1186–1201. doi:10.1109/TNN.2004.832830

    Article  Google Scholar 

  11. Avila-Mora IM, Castellanos-Sanchez C (2009) Bio-inspired clustering of moving objects. Glob Congr Intell Syst 58–62. doi:10.1109/GCIS.2009.445

  12. Baier V (2005) Motion perception with recurrent self-organizing maps based models. In: International joint conference on neural networks, pp 1182–1186. doi:10.1109/IJCNN.2005.1556021

  13. Mario I, Chacon M, Sergio Gonzalez D, Javier Vega P (2009) Simplified SOM-neural model for video segmentation of moving object. In: International joint conference on neural networks, pp 474–480. doi:10.1109/IJCNN.2005.1556021

  14. Ghasemi A, Safabakhsh R (2011) Unsupervised foreground–background segmentation using growing self organizing map in noisy backgrounds. In: International conference on computer research and development, pp 334–338. doi:10.1109/ICCRD.2011.5764031

  15. Maddalena L, Petrosino A (2008) A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans Image Proces 17:1168–1177. doi:10.1109/TIP.2008.924285

    Article  MathSciNet  Google Scholar 

  16. Wang Z, Bao H (2011) Cooperative neural network background model for multi-modal video surveillance. In: Seventh international conference on computational intelligence and security, pp 249–254. doi:10.1109/CIS.2011.63

  17. Culibrk D, Marques O, Socek D, Kalva H, Furht B (2007) Neural network approach to background modeling for video object segmentation. IEEE Trans Neural Networks 18:1614–1627. doi:10.1109/CIS.2011.63

    Article  Google Scholar 

  18. Chacon-Murguia Mario I, Urias-Zavala JD (2012) A comparison between a DTCNN and SOM like approach for dynamic object detection in videos. North Am Fuzzy Inf Process Soc 1–6. doi:10.1109/NAFIPS.2012.6291048

  19. Faro A, Giordano D, Spampinato C (2008) Evaluation of the traffic parameters in a metropolitan area by fusing visual perceptions and CNN processing of webcam images. IEEE Trans Neural Networks 19:1108–1129. doi:10.1109/TNN.2008.2000392

    Article  Google Scholar 

  20. Misra J, Saha I (2010) Artificial neural networks in hardware: a survey of two decades of progress. Neurocomputing 74:239–255. doi:10.1016/j.bbr.2011.03.031

    Article  Google Scholar 

  21. Cheng F-C, Huang S-C, Ruan S-J (2011) Scene analysis for object detection in advanced surveillance systems using Laplacian distribution model. IEEE Trans Syst Man Cybern-Part C: Appl Rev 41:589–598. doi:10.1109/TSMCC.2010.2092425

    Article  Google Scholar 

  22. Cheng F-C, Huang S-C, Ruan S-J (2011) Illumination-sensitive background modeling approach for accurate moving object detection. IEEE Trans Broadcast 57:794–801. doi:10.1109/TBC.2011.2160106

    Article  Google Scholar 

  23. Paruchuri JK, Sathiyamoorthy EP, Cheung SS, Chen C-H (2011) Spatially adaptive illumination modeling for background subtraction. In: IEEE international conference on computer vision, pp 1745–1752. doi:10.1109/ICCVW.2011.6130460

  24. Intachak T, Kaewapichai W (2011) Real-time illumination feedback system for adaptive background subtraction working in traffic video monitoring. In: International symposium on intelligent signal processing and communications, pp 1–5. doi:10.1109/ISPACS.2011.6146103

  25. Toyama K, Krumm J, Brumitt B, Meyers B (1999) Wallflower: principles and practice of background maintenance. In: International conference on computer vision, pp 255–261. doi:10.1109/ISPACS.2011.6146103

  26. Brutzer S, Höferlin B, Heidemann G (2011) Evaluation of background subtraction techniques for video surveillance. In: Conference on computer vision and pattern recognition, pp 1937–1944. doi:10.1109/CVPR.2011.5995508

  27. Miikkulainen R, Bednar JA, Choe Y, Sirosh J (2005) Computational maps in the visual cortex, Chap. 3 in Springer Sciences Media Inc.

  28. Bednar J (2012) Building a mechanistic model of the development and function of the primary visual cortex. J Physiol 106:194–211

    Google Scholar 

  29. De Paula BJ (2007) Modeling the self-organization of color-selective neurons in the visual cortex, Technical Report AI-TR-07-347

  30. Paplinski AP, Gustafsson L (2006) Feedback in multimodal self-organizing networks enhances perception of corrupted stimuli. Adv Artif Intell 4304:19–28. doi:10.1007/11941439_6

    Google Scholar 

  31. Luciw M, Weng J(J) (2012) Top-down connections in self-organizing Hebbian networks: topographic class grouping. IEEE Trans Auton Ment Dev 2:248–261. doi:10.1109/TAMD.2010.2072150

    Article  Google Scholar 

  32. Li L, Huang W, Gu IY-H, Tian Q (2004) Statistical modeling of complex backgrounds for foreground object detection. IEEE Trans Image Process 13:1459–1472. doi:10.1109/TIP.2004.836169

    Article  Google Scholar 

  33. Vacavant A, Chateau T, Wilhelm A, Lequièvre L (2013) A benchmark dataset for outdoor foreground/background extraction. Lecture notes on computer science, vol. 7728, pp 291–300. doi:10.1007/978-3-642-37410-4_25

  34. Honovich J (2011) Average frame rate used for recording. http://ipvm.com/updates/1100

  35. Vacavant A http://bmc.univ-bpclermont.fr/sites/default/files/results/Results-evaluation-real.png

  36. Yoshinaga S, Shimada A, Nagahara H, Taniguchi R (2014) Object detection based on spatiotemporal background models. Comput Vision Image Underst 122:84–91

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by Fondo Mixto de Fomento a la Investigacion Cientifica y Tecnologica CONACYT- Gobierno del Estado de Chihuahua and DGEST under grants CHIH-2012-C03-193760 and CHI-IET-2012-105, and CHI-MCIET-2013-230.

Author information

Authors and Affiliations

  1. Visual Perception Applications on Robotic Lab, Chihuahua Institute of Technology, Tecnologico Avenue 2909, Chihuahua, Mexico

    Juan A. Ramirez-Quintana & Mario I. Chacon-Murguia

Authors
  1. Juan A. Ramirez-Quintana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mario I. Chacon-Murguia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juan A. Ramirez-Quintana.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ramirez-Quintana, J.A., Chacon-Murguia, M.I. An Adaptive Unsupervised Neural Network Based on Perceptual Mechanism for Dynamic Object Detection in Videos with Real Scenarios. Neural Process Lett 42, 665–689 (2015). https://doi.org/10.1007/s11063-014-9380-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-014-9380-7

Keywords

Search

Navigation