Mean Response-Time Minimization of a Soft-Cascade Detector

Francisco Rodolfo Barbosa-Anda, Cyril Briand, Frédéric Lerasle, Alhayat Ali Mekonnen

2016

Abstract

In this paper, the problem of minimizing the mean response-time of a soft-cascade detector is addressed. A soft-cascade detector is a machine learning tool used in applications that need to recognize the presence of certain types of object instances in images. Classical soft-cascade learning methods select the weak classifiers that compose the cascade, as well as the classification thresholds applied at each cascade level, so that a desired detection performance is reached. They usually do not take into account its mean response-time, which is also of importance in time-constrained applications. To overcome that, we consider the threshold selection problem aiming to minimize the computation time needed to detect a target object in an image (i.e., by classifying a set of samples). We prove the NP-hardness of the problem and propose a mathematical model that takes benefit from several dominance properties, which are put into evidence. On the basis of computational experiments, we show that we can provide a faster cascade detector, while maintaining the same detection performances.

References

  1. Bourdev, L. and Brandt, J. (2005). Robust object detection via soft cascade. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR'05), volume 2, pages 236-243.
  2. Breitenstein, M., Reichlin, F., Leibe, B., Koller-Meier, E., and Van Gool, L. (2011). Online multiperson tracking-by-detection from a single, uncalibrated camera. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(9):1820-1833.
  3. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886- 893 vol. 1.
  4. Dollár, P. (2014). Piotr's Computer Vision Matlab Toolbox (PMT).
  5. Dollár, P., Appel, R., Belongie, S., and Perona, P. (2014). Fast feature pyramids for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(8):1532-1545.
  6. Dollár, P., Wojek, C., Schiele, B., and Perona, P. (2012). Pedestrian detection: An evaluation of the state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(4):743-761.
  7. Ess, A., Schindler, K., Leibe, B., and Van Gool, L. (2010). Object detection and tracking for autonomous navigation in dynamic environments. The International Journal of Robotics Research, 29(14):1707-1725.
  8. Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., and Zisserman, A. (2010). The pascal visual object classes (VOC) challenge. International Journal of Computer Vision, 88(2):303-338.
  9. Garey, M. R. and Johnson, D. S. (1979). Computers and Intractability: A Guide to the Theory of NPCompleteness. W. H. Freeman & Co., New York, NY, USA.
  10. Gerónimo, D., L ópez, A., Sappa, A., and Graf, T. (2010). Survey of pedestrian detection for advanced driver assistance systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(7):1239-1258.
  11. Jourdheuil, L., Allezard, N., Chateau, T., and Chesnais, T. (2012). Heterogeneous adaboost with real-time constraints - application to the detection of pedestrians by stereovision. In Proc. VISAPP, pages 539-546.
  12. Mekonnen, A. A., Lerasle, F., Herbulot, A., and Briand, C. (2014). People detection with heterogeneous features and explicit optimization on computation time. In International Conference on Pattern Recognition (ICPR'14), Stockholm, Sweden.
  13. Pan, H., Zhu, Y., and Xia, L. (2013). Efficient and accurate face detection using heterogeneous feature descriptors and feature selection. Computer Vision and Image Understanding, 117(1):12 - 28.
  14. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), pages 1-42.
  15. Schapire, R. E. (2003). The boosting approach to machine learning: An overview. Lecture Notes in Statistics, pages 149-172.
  16. Tang, D., Liu, Y., and kyun Kim, T. (2012). Fast pedestrian detection by cascaded random forest with dominant orientation templates. In Proceedings of the British Machine Vision Conference (BMVC'12), pages 58.1- 58.11. BMVA Press.
  17. Viola, P. A. and Jones, M. J. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2):137-154.
  18. Zhang, C. and Viola, P. A. (2008). Multiple-instance pruning for learning efficient cascade detectors. In Advances in Neural Information Processing Systems (NIPS'08), pages 1681-1688.
  19. Zhang, M. and Alhajj, R. (2009). Content-based image retrieval: From the object detection/recognition point of view. In Ma, Z., editor, Artificial Intelligence for Maximizing Content Based Image Retrieval, PA: Information Science Reference, pages 115-144. Hershey.
  20. Zhang, X., Yang, Y.-H., Han, Z., Wang, H., and Gao, C. (2013). Object class detection: A survey. ACM Comput. Surv., 46(1):10:1-10:53.
  21. Zhu, Q., Yeh, M.-C., Cheng, K.-T., and Avidan, S. (2006). Fast human detection using a cascade of histograms of oriented gradients. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR'06), New York, NY, USA.
Download


Paper Citation


in Harvard Style

Barbosa-Anda F., Briand C., Lerasle F. and Mekonnen A. (2016). Mean Response-Time Minimization of a Soft-Cascade Detector . In Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-171-7, pages 252-260. DOI: 10.5220/0005700702520260


in Bibtex Style

@conference{icores16,
author={Francisco Rodolfo Barbosa-Anda and Cyril Briand and Frédéric Lerasle and Alhayat Ali Mekonnen},
title={Mean Response-Time Minimization of a Soft-Cascade Detector},
booktitle={Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2016},
pages={252-260},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005700702520260},
isbn={978-989-758-171-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Mean Response-Time Minimization of a Soft-Cascade Detector
SN - 978-989-758-171-7
AU - Barbosa-Anda F.
AU - Briand C.
AU - Lerasle F.
AU - Mekonnen A.
PY - 2016
SP - 252
EP - 260
DO - 10.5220/0005700702520260