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Journal of Real-Time Image Processing

, Volume 16, Issue 2, pp 243–253 | Cite as

Real-time multi-class object detection using two-dimensional index

  • Yumin Dou
  • Pei Xu
  • Mao YeEmail author
  • Xue Li
  • Lishen Pei
  • Xudong Li
Original Research Paper
  • 212 Downloads

Abstract

When there exists only one sample for each category of objects, previous approaches of training multi-class classifiers are not applicable. In this paper, we propose a new template matching method that is both robust and real-time to multi-class object detections. Firstly, object features are encoded as binary codes based on both quantized gradient intensity and quantized gradient orientation mappings. Then, a two-dimensional index table is constructed. This two-dimensional index table has advantages in effectively organizing relationships between the features from the multi-class templates and their corresponding locations in the templates. For a target image, the features are firstly encoded. Then the object is localized by voting based on the queries of features from the index table. Our experiments on two public data sets demonstrate the high efficiency of our method and the superior performance to the state-of-the-art methods.

Keywords

Object detection Template matching Real time  Two-dimensional index table 

Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61375038).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Yumin Dou
    • 1
  • Pei Xu
    • 1
  • Mao Ye
    • 1
    Email author
  • Xue Li
    • 1
  • Lishen Pei
    • 1
  • Xudong Li
    • 1
  1. 1.School of Computer Science and Engineering, Center for RoboticsUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China

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