Publication detail

Local Rank Patterns - Novel Features for Rapid Object Detection

HRADIŠ, M. HEROUT, A. ZEMČÍK, P.

Original Title

Local Rank Patterns - Novel Features for Rapid Object Detection

English Title

Local Rank Patterns - Novel Features for Rapid Object Detection

Type

conference paper

Language

en

Original Abstract

This paper presents Local Rank Patterns (LRP) - novel features for rapid object detection in images which are based on existing features Local Rank Differences (LRD). The performance of the novel features is thoroughly tested on frontal face detection task and it is compared to the performance of the LRD and the traditionally used Haar-like features. The results show that the LRP surpass the LRD and the Haar-like features in the precision of detection and also in the average number of features needed for classification. Considering recent successful and efficient implementations of LRD on CPU, GPU and FPGA, the results suggest that LRP are good choice for object detection and that they could replace the Haar-like features in some applications in the future.

English abstract

This paper presents Local Rank Patterns (LRP) - novel features for rapid object detection in images which are based on existing features Local Rank Differences (LRD). The performance of the novel features is thoroughly tested on frontal face detection task and it is compared to the performance of the LRD and the traditionally used Haar-like features. The results show that the LRP surpass the LRD and the Haar-like features in the precision of detection and also in the average number of features needed for classification. Considering recent successful and efficient implementations of LRD on CPU, GPU and FPGA, the results suggest that LRP are good choice for object detection and that they could replace the Haar-like features in some applications in the future.

Keywords

WadlBoost, Local Rank Differences, Local Rank Patterns, object detection

RIV year

2008

Released

12.11.2008

Publisher

Springer Verlag

Location

Heidelberg

ISBN

0302-9743

Periodical

Lecture Notes in Computer Science

Year of study

NEUVEDEN

Number

12

State

DE

Pages from

1

Pages to

12

Pages count

12

URL

https://www.fit.vut.cz/research/publication/8838/

Documents

BibTex 


@inproceedings{BUT33447,
  author="Michal {Hradiš} and Adam {Herout} and Pavel {Zemčík}",
  title="Local Rank Patterns - Novel Features for Rapid Object Detection",
  annote="This paper presents Local Rank Patterns (LRP) - novel features for rapid object
detection in images which are based on existing features Local Rank Differences
(LRD). The performance of the novel features is thoroughly tested on frontal face
detection task and it is compared to the performance of the LRD and the
traditionally used Haar-like features. The results show that the LRP surpass the
LRD and the Haar-like features in the precision of detection and also in the
average number of features needed for classification. Considering recent
successful and efficient implementations of LRD on CPU, GPU and FPGA, the results
suggest that LRP are good choice for object detection and that they could replace
the Haar-like features in some applications in the future.",
  address="Springer Verlag",
  booktitle="Proceedings of International Conference on Computer Vision and Graphics 2008",
  chapter="33447",
  edition="Lecture Notes in Computer Science",
  howpublished="print",
  institution="Springer Verlag",
  journal="Lecture Notes in Computer Science (IF 0,513)",
  number="12",
  year="2008",
  month="november",
  pages="1--12",
  publisher="Springer Verlag",
  type="conference paper"
}