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Journal of Signal Processing Systems

, Volume 91, Issue 2, pp 117–129 | Cite as

High Performance Real-Time Pedestrian Detection Using Light Weight Features and Fast Cascaded Kernel SVM Classification

  • Muhammad BilalEmail author
  • Muhammad Shehzad Hanif
Article
  • 143 Downloads

Abstract

Fast and robust video based pedestrian detection has been an active research area in computer vision for the past many years and is still considered a challenging task. Despite appearance of several sophisticated algorithms performing conspicuously on standard datasets, the goal of achieving an acceptable detection performance in real-time scenario remains elusive. Earlier works in this regard have demonstrated that kernel support vector machine based classifiers can be integrated with low complexity integer-only features to yield powerful detectors achieving accuracy at par with the contemporary boosting cascades based detectors which employ complex features requiring floating point operations. The current work describes a new technique to implement a soft cascade to speed up evaluation of the kernel classifier. The proposed approach achieves rapid early rejection of the true negatives through identification of the most relevant feature components sorted by the estimated energies of their corresponding kernel functions. The proposed cascading scheme ensures that evaluation of only 4% of the features in the detection window leads to rejection of up to 62.8% and 61.8% true negatives on INRIA and ETH datasets respectively without noticeable effect on detection accuracy. Overall, a reduction of 90% and 89% in the computational load of kernel evaluation is observed on the mentioned datasets respectively while showing excellent generalization property. This technique combined with inclusion of multiple detectors for different scales and hardware support for parallelization and vector computation results in up to three times the processing speed on desktop as well as embedded processors compared to the contemporary detectors while achieving better or comparative detection accuracy.

Keywords

Real-time object detection Pedestrian detection Kernel support vector machine Histogram of oriented gradients Cascade classifier 

References

  1. 1.
    Bilal, M., Khan, A., Khan, M. U. K., & Kyung, C. M. (2017). A low complexity pedestrian detection framework for smart video surveillance systems. IEEE Transactions on Circuits and Systems for Video Technology, 27, 2260–2273.CrossRefGoogle Scholar
  2. 2.
    Wu, J. & Rehg, J. M. (2009). Beyond the Euclidean distance: Creating effective visual codebooks using the Histogram Intersection Kernel. In IEEE 12th International Conference on Computer Vision, Kyoto, Japan, pp. 630–637.Google Scholar
  3. 3.
    Dalal, N. & Triggs, B. (2005). Histograms of oriented gradients for human detection. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, pp. 886–893.Google Scholar
  4. 4.
    Bilal, M. (2017). Algorithmic optimization of histogram intersection kernel SVM-based pedestrian detection using low complexity features. IET Computer Vision, 11, 350–357.CrossRefGoogle Scholar
  5. 5.
    Dollar, P., Appel, R., Belongie, S., & Perona, P. (2014). Fast feature pyramids for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 1532–1545.CrossRefGoogle Scholar
  6. 6.
    Ess, A., Leibe, B., Schindler, K., & Gool, L. V. (2008). A mobile vision system for robust multi-person tracking. In IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, pp. 1–8.Google Scholar
  7. 7.
    Dollar, P., Wojek, C., Schiele, B., & Perona, P. (2012). Pedestrian detection: an evaluation of the state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 743–761.CrossRefGoogle Scholar
  8. 8.
    Zhang, S., Benenson, R., Omran, M., Hosang, J., & Schiele, B. (2016). How far are we from solving pedestrian detection? In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 1259–1267.Google Scholar
  9. 9.
    Benenson, R., Omran, M., Hosang, J., & Schiele, B. (2014). Ten years of pedestrian detection, what have we learned? In ECCV Workshop on Computer Vision for Road Scene Understanding and Autonomous Driving, Zurich, Switzerland, pp. 613–627.Google Scholar
  10. 10.
    Wang, X., Han, T. X., & Yan, S. (2009). An HOG-LBP human detector with partial occlusion handling. In IEEE 12th International Conference on Computer Vision, Kyoto, Japan, pp. 32–39.Google Scholar
  11. 11.
    Paisitkriangkrai, S., Shen, C., & Hengel, A. V. D. (2016). Pedestrian detection with spatially pooled features and structured ensemble learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 1243–1257.CrossRefGoogle Scholar
  12. 12.
    Watanabe, T. & Ito, S. (2013). Two Co-occurrence histogram features using gradient orientations and local binary patterns for pedestrian detection. In 2nd IAPR Asian Conference on Pattern Recognition, Okinawa, Japan, pp. 415–419.Google Scholar
  13. 13.
    Benenson, R., Mathias, M., Tuytelaars, T., & Gool, L. V. (2013). Seeking the strongest rigid detector. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Portland, Oregon, USA, pp. 3666–3673.Google Scholar
  14. 14.
    Zhang, S., Benenson, R., & Schiele, B. (2015). Filtered channel features for pedestrian detection. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, pp. 1751–1760.Google Scholar
  15. 15.
    Benenson, R., Mathias, M., Timofte, R., & Van Gool, L. (2012). Pedestrian detection at 100 frames per second. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, pp. 2903–2910.Google Scholar
  16. 16.
    Cheng, M. M., Zhang, Z., Lin, W. Y., & Torr, P. (2014). BING: binarized normed gradients for objectness estimation at 300fps. In 2014 I.E. Conference on Computer Vision and Pattern Recognition, pp. 3286–3293.Google Scholar
  17. 17.
    Yan, J., Zhang, X., Lei, Z., Liao, S., & Li, S. Z. (2013). Robust multi-resolution pedestrian detection in traffic scenes. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 3033–3040.Google Scholar
  18. 18.
    Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster (R-CNN): towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. pp. 1–1.Google Scholar
  19. 19.
    Zhang, L., Lin, L., Liang, X., & He, K. (2016). Is faster R-CNN doing well for pedestrian detection? In The 14th European Conference on Computer Vision, Amsterdam, The Netherlands, pp. 443–457.Google Scholar
  20. 20.
    Porikli, F. (2005). Integral histogram: a fast way to extract histograms in Cartesian spaces. In 2005 I.E. Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol. 1. pp. 829–836.Google Scholar
  21. 21.
    Zhu, Q., Yeh, M.-C., Cheng, K.-T., & Avidan, S. (2006). Fast human detection using a cascade of histograms of oriented gradients. In 2006 I.E. Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), pp. 1491–1498.Google Scholar
  22. 22.
    Bourdev, L., & Brandt, J. (2005). Robust object detection via soft cascade. In 2005 I.E. Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol. 2 pp. 236–243.Google Scholar
  23. 23.
    Zhang, C., & Viola, P. A. (2007). Multiple-instance pruning for learning efficient cascade detectors. In Neural Information Processing Systems (NIPS), Vancouver, Canada, pp. 1681–1688.Google Scholar
  24. 24.
    Yu, J., Miyamoto, R., & Onoye, T. (2013). A speed-up scheme based on multiple-instance pruning for pedestrian detection using a support vector machine. IEEE Transactions on Image Processing, 22, 4752–4761.MathSciNetCrossRefGoogle Scholar
  25. 25.
    Rätsch, M., Romdhani, S., & Vetter, T. (2004). Efficient face detection by a cascaded support vector machine using haar-like features. In Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30–September 1, 2004. Proceedings, C. E. Rasmussen, H. H. Bülthoff, B. Schölkopf, and M. A. Giese, Eds., ed Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 62–70.Google Scholar
  26. 26.
    Maji, S., Berg, A. C., & Malik, J. (2008). Classification using intersection kernel support vector machines is efficient. In IEEE Computer Society Computer Vision and Pattern Recognition, Anchorage, Alaska, USA, pp. 1–8.Google Scholar
  27. 27.
    Baek, J., Kim, J., & Kim, E. (2017). Fast and efficient pedestrian detection via the cascade implementation of an additive kernel support vector machine. IEEE Transactions on Intelligent Transportation Systems, 18, 902–916.CrossRefGoogle Scholar
  28. 28.
    Joachims, T. (1999). Making large-scale support vector machine learning practical. In Advances in kernel methods, S. Bernhard, lkopf, J. C. B. Christopher, and J. S. Alexander, Eds., ed: MIT Press, pp. 169–184.Google Scholar
  29. 29.
    Bradski, G. (2000, 1st September, 2016). The Open CV Library. Available: http://opencv.org/
  30. 30.
    Ben-Haim, G. (2012, 04–01-2017). Optimization of Image Processing Algorithms: A Case Study. Available: https://software.intel.com/en-us/articles/optimization-of-image-processing-algorithms-a-case-study

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia
  2. 2.Center of Excellence in Intelligent Engineering Systems (CEIES)King Abdulaziz UniversityJeddahSaudi Arabia

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