Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Skin Segmentation Using Color Pixel Classification: Analysis and Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting social interactions of the elderly in a nursing home environment
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Aging in place: fall detection and localization in a distributed smart camera network
Proceedings of the 15th international conference on Multimedia
Video-Based Fall Detection in the Home Using Principal Component Analysis
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Recognizing human activities from multi-modal sensors
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
Multimedia Tools and Applications
Automatic video-based human motion analyzer for consumer surveillance system
IEEE Transactions on Consumer Electronics
Activity Analysis, Summarization, and Visualization for Indoor Human Activity Monitoring
IEEE Transactions on Circuits and Systems for Video Technology
Broadcast Court-Net Sports Video Analysis Using Fast 3-D Camera Modeling
IEEE Transactions on Circuits and Systems for Video Technology
Development of a measurement and evaluation system for bed-making activity for self-training
DHM'13 Proceedings of the 4th International conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management: healthcare and safety of the environment and transport - Volume Part I
Hi-index | 0.10 |
This paper addresses the problem of assessing a trainee's performance during a simulated delivery training by employing automatic analysis of a video camera signal. We aim at providing objective statistics reflecting the trainee's behavior, so that the instructor is able to give valuable suggestions after the training. The basic idea is to analyze the moving and location parameters of the trainee, on which the behavior of the trainee can be judged and also compared. Our system consists of three major steps. In the first step, we label specific pixels with a given color, based on a Gaussian model. In the second step, the mean shift (MS) algorithm is employed to find the densest region of a color, where the center of that region indicates the center of a medical cap worn by a trainee. To accelerate the convergence of the MS algorithm, we propose to combine the distribution sampling and on-line mode updating based on the pyramid sampling technique. In the last step, we assume that the cap's position represents the position of a trainee. Therefore, several statistics, such as the moving trajectory and the total movement of each trainee, can be calculated. These statistics associated with the domain knowledge, help us to determine trainees' teamwork. Our system also enables an interactive way for instructors to choose the interested individual trainee, and then provides more results of him. Experimental evaluations using real delivery training videos demonstrate the effectiveness of the proposed work.