A fast online incremental learning method for object detection and pose classification using voting and combined appearance modeling

Authors:
Sirinart Tangruamsub;Keisuke Takada;Osamu Hasegawa
Affiliations:
Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama 226-8503, Japan;Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama 226-8503, Japan;Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama 226-8503, Japan
Venue:
Image Communication
Year:
2012

Citing 9
Cited 0

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Communications of the ACM
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Contour-Based Learning for Object Detection

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Putting Objects in Perspective

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Towards Multi-View Object Class Detection

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Techniques for still image scene classification and object detection

ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
TextonBoost: joint appearance, shape and context modeling for multi-class object recognition and segmentation

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
SURF: speeded up robust features

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel and rapid object detection method that identifies object positions and classifies object views. To overcome the limitations of appearance-based object recognition, we integrate a spatial relationship between local key points and object center position. A voting technique is applied to estimate the object area and then construct a bounding box to capture the object. A combined appearance model is introduced by a recall image to help deal with false detection problems. Experimental results show that our method can improve the object detection time while still preserving the average precision results. Moreover, our method can improve the accuracy of view classification.