Yet Even Faster (YEF) real-time object detection

Authors:
Yotam Abramson;Bruno Steux;Hicham Ghorayeb
Affiliations:
Robotics Center, Ecole des Mines de Paris, 60 bvd Saint Michel, 75006 Paris, France.;Robotics Center, Ecole des Mines de Paris, 60 bvd Saint Michel, 75006 Paris, France.;Robotics Center, Ecole des Mines de Paris, 60 bvd Saint Michel, 75006 Paris, France
Venue:
International Journal of Intelligent Systems Technologies and Applications
Year:
2007

Citing 4
Cited 3

Neural Network-Based Face Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Detection Using Wavelet Templates

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Robust Real-Time Face Detection

International Journal of Computer Vision
A core for ambient and mobile intelligent imaging applications

ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1

Classification of structural cartographic objects using edge-based features

ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
A data-driven detection optimization framework

Neurocomputing
A survey on recent object detection techniques useful for monocular vision-based planetary terrain classification

Robotics and Autonomous Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present an improvement to the object detection scheme of Viola and Jones (2004), using the example of face detection. We present a new kind of visual features that is based on sampling individual pixels within the examined sub-window, instead of comparing the sums of pixel values in rectangular regions. Our features are faster and do not demand the preparation of an integral image, nor variance-normalisation of each sub-window. The result is an improved system that detects faces in quarter-PAL images in only 9 milliseconds per image on a 2.4-GHz pentium computer.