Adaptive window search using semantic texton forests for real-time object detection

Yuki Ono, Abdul Raziz Junaidi, Tadahiro Kuroda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We propose a new window search method to realize real-time object detection. Our method generates windows adaptively for objects' shapes and scales to detect various size objects. It also achieves real-time window search by using fast estimation of object's location based on existence probability of an object. Experiment results demonstrate that the proposed method reduces the number of windows drastically compared with exhaustive search. Furthermore, our method reduces the processing time while maintaining recall compared with the state-of-the art method when the numbers of searched windows are same in Pascal VOC 2007 dataset.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Pages3293-3296
Number of pages4
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: 2013 Sep 152013 Sep 18

Publication series

Name2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Other

Other2013 20th IEEE International Conference on Image Processing, ICIP 2013
CountryAustralia
CityMelbourne, VIC
Period13/9/1513/9/18

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Keywords

  • adaptive window search
  • object detection
  • object map
  • real-time

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Ono, Y., Junaidi, A. R., & Kuroda, T. (2013). Adaptive window search using semantic texton forests for real-time object detection. In 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings (pp. 3293-3296). [6738678] (2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings). https://doi.org/10.1109/ICIP.2013.6738678