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Temporally Integrated Pedestrian Detection from Non-stationary Video

  • Chi-Jiunn Wu
  • Shang-Hong Lai
Conference paper
  • 676 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4351)

Abstract

In this paper, we propose a novel approach for detecting pedestrians from video sequence acquired with non-static camera. The proposed algorithm consists of three major components, including global motion estimation with motion-compensated frame subtraction, AdaBoost pedestrian detection, and temporal integration. The global motion estimation with frame subtraction can reduce the influence of the background pixels and improve the detection accuracy and efficiency. The simplified affine model is used to fit the global motion model from some reliable blocks by using the RANSAC robust estimation algorithm. After motion-compensated frame subtraction, the AdaBoost classifier is employed to detection pedestrians in a single frame. At last, the graph structure is applied to model the relationship of different detection windows in the temporal domain. Similar detected windows are grouped as the same clusters by using the optimal linking algorithm. The missed detection windows will be recovered from the object clustering results. Finally, we show the experimental results by using the proposed pedestrian detection algorithm on some real video sequences to demonstrate its high detection accuracy and low false alarm rate.

Keywords

Video Sequence Temporal Integration Global Motion Detection Window Human Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chi-Jiunn Wu
    • 1
  • Shang-Hong Lai
    • 1
  1. 1.Department of Computer ScienceNational Tsing Hua UniversityHsinchuTaiwan

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