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
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"
}