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Multiple Instance Boost Using Graph Embedding Based Decision Stump for Pedestrian Detection

  • Junbiao Pang
  • Qingming Huang
  • Shuqiang Jiang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5305)

Abstract

Pedestrian detection in still image should handle the large appearance and stance variations arising from the articulated structure, various clothing of human as well as viewpoints. In this paper, we address this problem from a view which utilizes multiple instances to represent the variations in multiple instance learning (MIL) framework. Specifically, logistic multiple instance boost (LMIBoost) is advocated to learn the pedestrian appearance model. To efficiently use the histogram feature, we propose the graph embedding based decision stump for the data with non-Gaussian distribution. First the topology structure of the examples are carefully designed to keep between-class far and within-class close. Second, K-means algorithm is adopted to fast locate the multiple decision planes for the weak classifier. Experiments show the improved accuracy of the proposed approach in comparison with existing pedestrian detection methods, on two public test sets: INRIA and VOC2006’s person detection subtask [1].

Keywords

Support Vector Machine Multiple Instance Linear Support Vector Machine Histogram Feature Pedestrian 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 2008

Authors and Affiliations

  • Junbiao Pang
    • 1
    • 2
    • 3
  • Qingming Huang
    • 1
    • 2
    • 3
  • Shuqiang Jiang
    • 2
    • 3
  1. 1.Graduate university of Chinese Academy of SciencesBeijingChina
  2. 2.Key Lab. of Intelligent Information ProcessingChinese Academy of Sciences (CAS) 
  3. 3.Institute of Computing TechnologyCASBeijingChina

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