Local Rank Patterns --- Novel Features for Rapid Object Detection

Authors:
Michal Hradis;Adam Herout;Pavel Zemcik
Affiliations:
Graph@FIT, Brno University of Technology, Brno,;Graph@FIT, Brno University of Technology, Brno,;Graph@FIT, Brno University of Technology, Brno,
Venue:
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
Year:
2008

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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.