Head Pose Classification by Multi-Class AdaBoost with Fusion of RGB and Depth Images
Paper i proceeding, 2014

This paper addresses issues in multi-class visual object classification, where sequential learning and sensor fusion are exploited in a unified framework. We adopt a novel method for head pose classification using RGB and depth images. The main contribution of this paper is a multi-class AdaBoost classification framework where information obtained from RGB and depth modalities interactively complement each other. This is achieved by learning weak hypotheses for RGB and depth modalities independently with the same sampling weight in the boosting structure, and then fusing them through learning a sub-ensemble. Experiments are conducted on a Kinect RGB-D face image dataset containing 4098 face images in 5 different poses. Results have shown good performance in obtaining high classification rate (99.76%) with low false alarms on the dataset.

RGB and depth images

AdaBoost

visual information fusion

head pose classification

Författare

Yixiao Yun

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Mohamed Hashim Changrampadi

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Irene Yu-Hua Gu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

1st International Conference on Signal Processing and Integrated Networks (SPIN)

174-177

Styrkeområden

Informations- och kommunikationsteknik

Transport

Ämneskategorier

Systemvetenskap

Signalbehandling

Datorseende och robotik (autonoma system)

ISBN

978-1-4799-2866-8

Mer information

Skapat

2017-10-08