Human Detection for a Video Surveillance Applied in the ‘SmartMonitor’ System

  • Dariusz Frejlichowski
  • Katarzyna Gościewska
  • Paweł Forczmański
  • Radosław Hofman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8671)


Human detection is one of the key and crucial tasks in video surveillance systems and is important for the purpose of object tracking, fall detection, human gait analysis or abnormal event detection. This paper concerns the application of two classifiers for human detection in the ‘SmartMonitor’ system — an intelligent security system based on image analysis. The classifiers are based on the Histogram of Oriented Gradients (HOG) descriptor and simple Haar-like features. The paper provides a brief description of the main system characteristics, discusses the problem of human detection and includes some results of the experiments performed using various parameters of HOG and Haar classifiers that were trained using benchmark databases and tested using appropriate video sequences. The paper aims at investigating the effectiveness and performance of both methods applied separately before incorporating them into the ‘SmartMonitor’ system’s video processing model.


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  1. 1.
    Frejlichowski, D., Forczmański, P., Nowosielski, A., Gościewska, K., Hofman, R.: SmartMonitor: An Approach to Simple, Intelligent and Affordable Visual Surveillance System. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2012. LNCS, vol. 7594, pp. 726–734. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Frejlichowski, D., Gościewska, K., Forczmański, P., Nowosielski, A., Hofman, R.: SmartMonitor: recent progress in the development of an innovative visual surveillance system. J. of Theoretical and Applied Computer Science 6(3), 28–35 (2012)Google Scholar
  3. 3.
    Frejlichowski, D., Gościewska, K., Forczmański, P., Nowosielski, A., Hofman, R.: Extraction of the Foreground Regions by Means of the Adaptive Background Modelling Based on Various Colour Components for a Visual Surveillance System. In: Kott, L. (ed.) ICALP 1986. LNCS, vol. 226, pp. 351–360. Springer, Heidelberg (1986)Google Scholar
  4. 4.
    Frejlichowski, D., Gościewska, K., Forczmański, P., Nowosielski, A., Hofman, R.: The Removal of False Detections from Foreground Regions Extracted Using Adaptive Background Modelling for a Visual Surveillance System. In: Saeed, K., Chaki, R., Cortesi, A., Wierzchoń, S. (eds.) CISIM 2013. LNCS, vol. 8104, pp. 253–264. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Frejlichowski, D., Gościewska, K., Forczmański, P., Hofman, R.: “SmartMonitor” — An Intelligent Security System for the Protection of Individuals and Small Properties with the Possibility of Home Automation. Sensors 14, 9922–9948 (2014)CrossRefGoogle Scholar
  6. 6.
    Borges, P.V.K., Conci, N., Cavallaro, A.: Video-Based Human Behavior Understanding: A Survey. IEEE T. Circ. Syst. Vid. 23(11), 1993–2008 (2013)CrossRefGoogle Scholar
  7. 7.
    Zhang, D., Peng, H., Haibin, Y., Lu, Y.: Crowd Abnormal Behavior Detection Based on Machine Learning. Information Technology Journal 12, 1199–1205 (2013)CrossRefGoogle Scholar
  8. 8.
    Vishwakarma, V., Mandal, C., Sural, S.: Automatic Detection of Human Fall in Video. In: Ghosh, A., De, R.K., Pal, S.K. (eds.) PReMI 2007. LNCS, vol. 4815, pp. 616–623. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Hou, Y.-L., Pang, G.K.H.: People Counting and Human Detection in a Challenging Situation. IEEE T. Syst. Man Cy. A 41(1), 24–33 (2011)CrossRefGoogle Scholar
  10. 10.
    Karpagavalli, P., Ramprasad, A.V.: Estimating the Density of the People and Counting the number of people in a crowd environment for human safety. In: 2013 International Conference on Communications and Signal Processing (ICCSP), pp. 663–667 (2013)Google Scholar
  11. 11.
    Paul, M., Haque, S.M.E., Chakraborty, S.: Human detection in surveillance video and its applications — a review. EURASIP Journal on Advanced in Signal Processing 2013(1), 1–16 (2013)CrossRefGoogle Scholar
  12. 12.
    Viola, P., Jones, M.: Rapid Object Detection Using a Boosted Cascade of Simple Features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 511–518 (2001)Google Scholar
  13. 13.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)Google Scholar
  14. 14.
    Lienhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection. In: Proceedings of 2002 International Conference on Image Processing, vol. 1, pp. I-900–I-903 (2002)Google Scholar
  15. 15.
    Pavani, S.-K., Delgado, D., Frangi, A.F.: Haar-like features with optimally weighted rectangles for rapid object detection. Pattern Recogn. 43, 160–172 (2010)Google Scholar
  16. 16.
    Hoang, V.-D., Le, M.-H., Jo, K.-H.: Fast human detection based on parallelogram haar-like features. In: IECON 2012 – 38th Annual Conference on IEEE Industrial Electronics Society, pp. 4220–4225 (2012)Google Scholar
  17. 17.
    Kushwaha, A.K.S., Sharma, C.M., Khare, M., Srivastava, R.K., Khare, A.: Automatic multiple human detection and tracking for visual surveillance system. In: 2012 International Conference on Informatics, Electronics & Vision (ICIEV), pp. 326–331 (2012)Google Scholar
  18. 18.
    Zhu, Q., Avidan, S., Yeh, M.-C., Cheng, K.-T.: Fast Human Detection Using Cascade of Histograms of Oriented Gradients. In: Proceeding of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1491–1498 (2006)Google Scholar
  19. 19.
    Huang, S.-S., Tsai, H.-M., Hsiao, P.-Y., Tu, M.-Q., Jian, E.-L.: Combining Histograms of Oriented Gradients with Global Feature for Human Detection. In: Lee, K.-T., Tsai, W.-H., Liao, H.-Y.M., Chen, T., Hsieh, J.-W., Tseng, C.-C. (eds.) MMM 2011 Part II. LNCS, vol. 6524, pp. 208–218. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  20. 20.
    Conde, C., Moctezuma, D., De Diego, I.M., Cabello, E.: HoGG: Gabor and HoG-based human detection for surveillance in non-controlled environments. Neurocomputing 100, 19–30 (2013)CrossRefGoogle Scholar
  21. 21.
    Keller, C.G., Enzweiler, M., Gavrila, D.M.: A New Benchmark for Stereo-Based Pedestrian Detection. In: 2011 IEEE Intelligent Vehicles Symposium (IV), pp. 691–696 (2011)Google Scholar
  22. 22.
    Kim, J., Han, Y., Hahn, H.: Human Detection using Projected Edge Feature. World Academy of Science, Engineering and Technology, International Science Index 60 5(12), 1672–1675 (2011)Google Scholar
  23. 23.
    Forczmański, P., Seweryn, M.: Surveillance Video Stream Analysis Using Adaptive Background Model and Object Recognition. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010, Part I. LNCS, vol. 6374, pp. 114–121. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dariusz Frejlichowski
    • 1
  • Katarzyna Gościewska
    • 1
    • 2
  • Paweł Forczmański
    • 1
  • Radosław Hofman
    • 2
  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of Technology, SzczecinSzczecinPoland
  2. 2.Smart Monitor sp. z o.o.SzczecinPoland

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