Head Pose Detection for a Wearable Parrot-Inspired Robot Based on Deep Learning

Abstract

Abstract:Extensive research has been conducted in human head pose detection systems and several applications have been identified to deploy such systems. Deep learning based head pose detection is one such method which has been studied for several decades and reports high success rates during implementation. Across several pet robots designed and developed for various needs, there is a complete absence of wearable pet robots and head pose detection models in wearable pet robots. Designing a wearable pet robot capable of head pose detection can provide more opportunities for research and development of such systems. In this paper, we present a novel head pose detection system for a wearable parrot-inspired pet robot using images taken from the wearer's shoulder. This is the first time head pose detection has been studied in wearable robots and using images from a side angle. In this study, we used AlexNet convolutional neural network architecture trained on the images from the database for the head pose detection system. The system was tested with 250 images and resulted in an accuracy of 94.4% across five head poses, namely left, left intermediate, straight, right, and right intermediate.

Cascaded Machine-Learning Technique for Debris Classification in Floor-Cleaning Robot Application

“…The area is used as a measure of the size of the spill. The area is estimated using a transformation as given in Equation (11).…”

“…Deep-learning-based computer vision is an emerging technique for automatic recognition and classification. Deep-learning architectures have many hidden layers of neurons and the neural network architecture can be modified and optimized to solve different complex tasks in computer vision-based applications [10][11][12][13][14][15][16][17]. Chen et al [10] proposed a computer vision-based robot grasping system for automatically sorting garbage where a Fast RCNN is employed for detecting different objects in the scene.…”

“…Chen et al [10] proposed a computer vision-based robot grasping system for automatically sorting garbage where a Fast RCNN is employed for detecting different objects in the scene. Deep-learning-based head-pose detection is proposed by Jaishankar et al for wearable pet robots [11]. The authors use AlexNet CNN architecture for implementing the head-pose detection system.…”