Car-pose detection using randomized WLD

Lei Lei, Yi Hu, Dae Hwan Kim, Sung-Jea Ko

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

Abstract

In both vehicle detection and vehicle tracking, the orientation of car will provide useful information to predict the trajectory. In this paper, we propose a method to determine the orientation of car in a still image. We train a set of Randomized Weber Local Descriptor (RWLD) based classifiers to overcome this problem. To make the system robust and fast, we also propose a tree structure to organize the classifiers to a pose estimator. We evaluate our method on a database consisting of more than 2000 vehicle images. The experimental results show that our method is effective. This pose estimator can be used for a variety of applications conveniently.

Original languageEnglish
Title of host publicationProceedings - 4th International Congress on Image and Signal Processing, CISP 2011
Pages1499-1502
Number of pages4
Volume3
DOIs
Publication statusPublished - 2011 Dec 1
Event4th International Congress on Image and Signal Processing, CISP 2011 - Shanghai, China
Duration: 2011 Oct 152011 Oct 17

Other

Other4th International Congress on Image and Signal Processing, CISP 2011
CountryChina
CityShanghai
Period11/10/1511/10/17

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Keywords

  • car-pose detection
  • pose estimation
  • WLD

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Lei, L., Hu, Y., Kim, D. H., & Ko, S-J. (2011). Car-pose detection using randomized WLD. In Proceedings - 4th International Congress on Image and Signal Processing, CISP 2011 (Vol. 3, pp. 1499-1502). [6100460] https://doi.org/10.1109/CISP.2011.6100460