Skip to main content
SpringerLink
Log in

A scene-adaptive motion detection model based on machine learning and data clustering

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Due to its wide applications and importance in computer vision, motion detection has been receiving considerable attention from industry and academy. However, previous motion detection algorithms fail to achieve the flexibility and accuracy simultaneously for good detection results. In the present work, a scene-adaptive motion detection model based on machine learning and clustering technology is proposed. This model begins with training to the system by a group of testing images, in terms of various accurate parameters of one certain scene. Significant modifications have been reserved in the same area during motion detection, which are considered as a change clustering. Then, the model takes advantage of clustering technology to generate a minimum spanning tree (MST), which is one kind of average linkage clustering. The average shortest distance of the minimum spanning tree serves as a benchmark to identify the change in images. Finally the training parameters and detection algorithm are combined to monitor the scene. The clustering is introduced to this model during sample training, in order to obtain factors of higher quality followed by more accurate detection results. Finally, the experiment confirms the excellent adaptability and precision of the proposed motion detection model.

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

Similar content being viewed by others

References

  1. Alpaydm E (2004) Introduction to machine learning. The MIT Press, MD

    Google Scholar 

  2. Barron JL, Fleet DJ, Beauchemin SS (1994) Performance of optical flow techniques. Int J Comput Vis 12:43–77

    Article  Google Scholar 

  3. Bishop CM (2006) Pattern recognition and machine learning. Springer, MD

    MATH  Google Scholar 

  4. Chan PK, Mahoney MV, Arshad MH (2003) “A machine learning approach to anomaly detection.” Technical Report, CS-2003-06

  5. Chaudhary A, Raheja JL, Das K, Raheja S (2011) Intelligent approaches to interact with machines using hand gesture recognition in natural way: a survey. Int J Comput Sci Eng Surv 2(1):122–133

    Article  Google Scholar 

  6. Chen D, Yang J (2007) Robust object tracking via online dynamic spatial bias appearance models. IEEE Trans Pattern Anal Mach Intell 29(12):2157–2169

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Comanicu D, Ramesh V, Meer P (2000) “Real-time tracking of non-rigid objects using mean shift.” Proc of International Conference on Computer Vision and Pattern Recognition: 142–149

  9. Cutler R, Davis L (1998) “View-based detection and analysis of periodic motion.” Proc. of the Fourteenth International Conference on Pattern Recognition(ICPR98) 1:495–500

  10. Dixon KR, Lippitt CE, Forsythe JC (2005) “Supervised machine learning for modeling human recognition of vehicle-driving situation.” Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS’05): 604–609

  11. Dong H, Giakoumidis N, Juma JB, Tretyakov DA, Mavridis N (2012) A fast laser motion detection and approaching behavior monitoring method of moving object alarm system(MOAS). Procedia Eng 41:749–756

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Fablet R, Bouthemy P, Gelgon M (1999) “Moving object detection in color image sequences using region-level graph labeling.” Proc. of the IEEE International Conference on Image Processing(ICIP’99) 2:939–943

  14. Fung G (2001) “A comprehensive overview of basic clustering algorithm”

  15. Gall J, Yao A, Razavi N, Van Gool L, Lempitsky V (2011) Hough forests for object detection, tracking and action recognition. IEEE Trans Pattern Anal Mach Intell 33(11):2188–2202

    Article  Google Scholar 

  16. Guanglong H, Shiyin Q (2012) High precision detection of a mobile object under dynamic imaging based on SURF and mean shift. CAAI Trans Intell Syst 7(1):61–68

    Google Scholar 

  17. Haritaoglu I, Harwood D, Davis LS (1998) “W4: who? when? where? what? a real time system for detecting and tracking people.” Proc. of the Third IEEE International Conference on Automatic Face and Gesture Recognition(ICAFGR’98): 222–227

  18. Hu T, Zhu L (2010) “The moving detection algorithm based on clustering technology.” Proc. of the International Conference on Computer and Communication Technologies in Agriculture Engineering(CCTAE’10): 83–186

  19. Jain R, Kasturi R, Schunck BG (1995) Machine vision. McGraw-Hill Inc, MD

    Google Scholar 

  20. Kentaro T, Krumm J, Brumitt B, Meyers B (1999) “Principles and practice of background maintenance.” Proc. of the Seventh IEEE International Conference on Computer Vision(ICCV’99) 1:255–261

  21. Olson CF (1995) Parallel algorithm for hierarchical clustering. Parallel Comput 21(8):1313–1325

    Article  MATH  MathSciNet  Google Scholar 

  22. Piciarelli C, Foresti GL (2011) Surveillance-oriented event detection in video streams. Intell Syst 26(3):32–41

    Article  Google Scholar 

  23. Richard J, Radke S, AI-Kofahi O, Roysam B (2005) Image change detection algorithms: a systematic survey. IEEE Trans Image Process 14(3):294–307

    Article  MathSciNet  Google Scholar 

  24. Scott DW (1992) Multivariate density estimation: theory, practice, and visualization. A Wiley-Interscience Publication, MD

    MATH  Google Scholar 

  25. Shafe AA, Hafz F, Ali MH (2009) Motion detection techniques using potical flow. World Acad Sci Eng Technol 32:559–561

    Google Scholar 

  26. Stenger B, Ramesh V, Paragios N, Coetzee F, Buhmann JM (2001) “Topology free hidden Markov models: application to back-ground modeling.” Proc. of the Eighth IEEE International Conference on Computer Vision(ICCV’01) 1:294–301

  27. Witten I, Frank E (2005) Data mining. Morgan Kaufmann press, MD

    MATH  Google Scholar 

  28. Zhou Z, Niu B, Ke C, Wu W (2010) “Static object tracking in road panoramic videos.” Proc. of 2010 I.E. International Symposium on Multimedia: 57–64

Download references

Acknowledgments

This work was financially supported by the Program of Education Commission of Hubei Province, China (Grant No.Q20131904), the National Natural Science Foundation of China (Grant No. 61261016), the National Culture Promotion Project of China (Grant No.201307)and the Program of Ethnic and Religious Affairs Commission of Hubei Province, China (Grant No.HBMW2012006). Moreover, during the research, many valuable suggestions have been provided by Professor May Huang and Dr. Eric Chen, International Technological University, USA.

Author information

Authors and Affiliations

  1. School of Information Engineering, Hubei University for Nationalities, Enshi, Hubei, China

    Tao Hu, Minghui Zheng, Jun Li & Li Zhu

  2. Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool, L16 9JD, UK

    Jia Hu

Authors
  1. Tao Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Minghui Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jia Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Minghui Zheng.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hu, T., Zheng, M., Li, J. et al. A scene-adaptive motion detection model based on machine learning and data clustering. Multimed Tools Appl 74, 2821–2839 (2015). https://doi.org/10.1007/s11042-013-1741-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-013-1741-0

Keywords

Search

Navigation