Naoto Date

Real-time human motion sensing based on vision-based inverse kinematics for interactive applications

Vision-based human motion sensing has a strong merit that it does not impose any physical restrictions on humans, which provides a natural way of measuring human motion. However, its real-time processing is not easy to realize, because a human body has a high degrees of freedom, whose vision-based analysis is not simple and is usually time consuming. Here, we have developed a method in which human postures are analyzed from a limited number of visual cues. It is a combination of numerical analysis of inverse kinematics and visual search. Our method is based on a general framework of inverse kinematics, and, therefore, we can use relatively complex human figure model, which can generate natural human motion. In our experimental studies, we show that our implemented system works in real-time on a PC-cluster.

Vision-based real-time motion capture system using multiple cameras

In this paper, we discuss a vision-based real-time motion capture system, which is constructed on a PC-cluster. Vision-based motion capture does not impose physical restrictions on humans, which provides a natural way of measuring human motion. However, there are several issues to be solved, which are robust estimation of human motion from a limited number of visual cues and computation cost of the estimation algorithm. To deal with these issues, we have developed multi-view-based algorithms using multiple cameras and have implemented the algorithms on a PC-cluster to solve the computation problem. In this paper, we present our experimental study on vision-based real-time motion capture with emphasis on 3D human posture estimation.

Confidence-driven architecture for real-time vision processing and its application to efficient vision-based human motion sensing

In this paper, we discuss a real-time vision architecture which provides a mechanism of controlling trade-off between the accuracy and the latency of vision systems. In vision systems, to acquire accurate information from input-images, the huge amount of computation power is usually required. On the other hand, to realize real-time processing, we must reduce the latency. Therefore, under given hardware resources, we must make difficult trade-off between the accuracy and the latency so that the quality of the systems output keeps appropriateness. To solve the problem, we propose confidence-driven scheme, which enables us to control the trade-off dynamically and easily without rebuilding vision systems. In the confidence-driven architecture, the trade-off can be controlled by specifying a generalized parameter called confidence, which relatively indicates how accurate the analysis should be. Here, we present the concept of confidence-driven architecture, and then, we show a shared memory which uses confidence-driven scheme. Using confidence-driven memory, we can use imprecise computation model to reduce the latency without a large decrease of accuracy.

Performance evaluation of vision-based real-time motion capture

In this paper, we discuss a vision-based real-time motion capture system, which is constructed on a PC-cluster. Vision-based motion capture is a merit that it does not impose any physical restrictions on humans, which provides a natural way of measuring human motion. However, there are several issues to be solved, which are robust estimation of human motion from limited number of visual cues, computation cost of the estimation algorithm. To deal with these issues, we have developed multi-view-based algorithms using multiple cameras and we have implemented the algorithms on a PC-cluster to solve the computation problem. In this paper, we present our experimental study on vision-based real-time motion capture with emphasis on 3D human posture estimation.