Abstract
In this paper, we propose a motion model that focuses on the discriminative parts of the human body related to target motions to classify human motions into specific categories, and apply this model to multi-class daily motion classifications. We extend this model to a motion recognition system which generates multiple sentences associated with human motions. The motion model is evaluated with the following four datasets acquired by a Kinect sensor or multiple infrared cameras in a motion capture studio: UCF-kinect; UT-kinect; HDM05-mocap; and YNL-mocap. We also evaluate the sentences generated from the dataset of motion and language pairs. The experimental results indicate that the motion model improves classification accuracy and our approach is better than other state-of-the-art methods for specific datasets, including human–object interactions with variations in the duration of motions, such as daily human motions. We achieve a classification rate of 81.1% for multi-class daily motion classifications in a non cross-subject setting. Additionally, the sentences generated by the motion recognition system are semantically and syntactically appropriate for the description of the target motion, which may lead to human–robot interaction using natural language.
Highlights
As the result of a change of social demand from industrial uses to service uses, robots and systems have become more intelligent and are a familiar presence in our daily lives
We extend the Fisher vectors (FVs)-hidden Markov model (HMM)/MLK-support vector machine (SVM) motion model described in the previous chapter to a motion recognition system that represents human motions as multiple sentences (Takano and Nakamura 2015)
We evaluated the effect of two types of local skeleton features (LSFs) on the UTkinect, UCF-kinect and HDM05-mocap datasets
Summary
As the result of a change of social demand from industrial uses to service uses, robots and systems have become more intelligent and are a familiar presence in our daily lives. Along with this change, intelligent robots and systems used in human living areas should be expected to have the abilities to observe humans closely, understand human behavior, grasp their intentions and give proper livelihood support. Optical motion capture systems provide accurate 3D skeleton markers of motion by using multiple infrared cameras. These systems are limited to use in motion capture studios and subjects have to wear cumbersome devices while performing motions. The release of low-cost and marker-less motion sensors, such as the Kinect developed by Microsoft, has recently made skeleton-position extractions much easier and more practical for skeleton-based motion classification (Shotton et al 2013). Presti and Cascia (2016) have reviewed the many works related to skeleton-based motion classification
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