Multimedia Tools and Applications

, Volume 78, Issue 3, pp 3781–3795 | Cite as

Combining static and dynamic features for real-time moving pedestrian detection

  • Yingjun Jiang
  • Jianxin Wang
  • Yixiong Liang
  • Jiazhi XiaEmail author


Pedestrian detecting and tracking are critical techniques in video monitoring. However, real-time pedestrian detection is still challenging in surveillance videos with complex background. In existing frameworks, feature extractions are usually time-consuming to achieve high detection accuracy. In this paper, we propose to combine sparse static and dynamic features to improve the feature extraction speed while keeping high detection accuracy. Firstly, the static sparse feature is extracted using a fast feature pyramid in each frame. Secondly, sparse optical flow is used to extract sparse dynamic feature among successive frames. Thirdly, we combine the two types of feature in the Adaboost classification. Experiments show that the average miss rate of our approach is 17%. The detection rate is up to 22 fps in a Matlab implementation. It shows that our approach achieves optimal detection accuracy compared to the state-of-the-art real-time pedestrian detection algorithms.


Pedestrian detection Combined feature Sparse optical flow 



This research is partially supported by National Nature Science Foundation of China (61309009) and Natural Science Foundation of Hunan Province, China (14JJ2008).


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Copyright information

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

Authors and Affiliations

  • Yingjun Jiang
    • 1
  • Jianxin Wang
    • 1
  • Yixiong Liang
    • 1
  • Jiazhi Xia
    • 1
    Email author
  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina

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