Boosted HOG Features and Its Application on Object Movement Detection

  • Junzo Watada
  • Huiming ZhangEmail author
  • Haydee Melo
  • Diqing Sun
  • Pandian Vasant
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 81)


Nowadays, traffic accidents is universally decreasing due to many advanced safety vehicle systems. To prevent the occurrence of a traffic accident, the first function that a safety vehicle system should accomplish is the detection of the objects in traffic situation. This paper presents a popular method called boosted HOG features to detect the pedestrians and vehicles in static images. We compared the differences and similarities of detecting pedestrians and vehicles, then we use boosted HOG features to get an satisfying result. In detecting pedestrians part, Histograms of Oriented Gradients (HOG) feature is applied as the basic feature due to its good performance in various kinds of background. On that basis, we create a new feature with boosting algorithm to obtain more accurate result. In detecting vehicles part, we use the shadow underneath vehicle as the feature, so we can utilize it to detect vehicles in daytime. The shadow is the important feature for vehicles in traffic scenes. The region under vehicle is usually darker than other objects or backgrounds and could be segmented by setting a threshold.


Pedestrian detection Vehicle detection Hog feature 



This work was supported in part by Grants-in-Aid for Scientific Research, MEXT (No.23500289), and parially by Peronas Corpolation, Petroleum Research Fund (PRF) No.0153AB-A33.


  1. 1.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)Google Scholar
  2. 2.
    Zhu, Q., Yeh, M.-C., Cheng, K.-T., Avidan, S.: Fast human detection using a cascade of histograms of oriented gradients. In: Computer Vision and Pattern Recognition, vol. 2, pp. 1491–1498 (2006)Google Scholar
  3. 3.
    Tuzel, O., Porikli, F., Meer, P.: Human detection via classification on riemannian manifolds. In: Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar
  4. 4.
    Wojek, C., Schiele, B.: A performance evaluation of single and multi-feature people detection. In: Proceedings of the 30th DAGM Symposium on Pattern Recognition, pp. 82-91 (2008)Google Scholar
  5. 5.
    Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: The 9th ICCV, Nice, France, vol. 1, pp. 734–741 (2003)Google Scholar
  6. 6.
    Li, X., Guo, X.-S., Guo, J.-B.: A Multi-feature fusion method for forward vehicle detection with single camera. In: The International Conference on Mechatronics and Industrial Informatics, pp. 998–1004 (2003)Google Scholar
  7. 7.
    Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection an evaluation of the state of the art. Pattern Anal. Mach. Intell. 34, 743–761 (2012)CrossRefGoogle Scholar
  8. 8.
    Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: a benchmark. In: Computer Vision and Pattern Recognition, pp. 304–311 (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Junzo Watada
    • 1
  • Huiming Zhang
    • 2
    Email author
  • Haydee Melo
    • 2
  • Diqing Sun
    • 2
  • Pandian Vasant
    • 3
  1. 1.Computer and Information Sciences DepartmentPETRONAS University of TechnologySeri IskandarMalaysia
  2. 2.Graduate School of Information, Production and SystemsWaseda UniversityKitakyushuJapan
  3. 3.Fundamental and Applied Sciences DepartmentPETRONAS University of TechnologySeri IskandarMalaysia

Personalised recommendations