Development of the Joint Attention with a New Face Tracking Method for Multiple People

• Main Authors: Hung-Jing Jian, 簡宏景
• Other Authors: Han-Pang Huang
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/58834582566498946748

碩士 === 國立臺灣大學 === 機械工程學研究所 === 95 === This thesis aims to develop a system for multiple objects tracking and joint attention between people and robot. We propose a new method (Modified Multi-CAMSHIFT, MMC), which is based on the characteristics of color and shape probability distribution, to solve the tracking problems for multiple objects. The color cue information is calculated by MMC that improves from CAMSHIFT theory. And the shape cue information is calculated by procedure of Scharr kernel mask. Then we calculate out color histogram and orientation histogram respectively, and use the Adaptive Feature Selection for optimal tracking. For judging face or non-face regions, we have included Eyes-pair Fast Extracting. Our proposed MMC is based on adaptive multi-resolution (AMR) framework for reducing computation. The experimental results show that based on all the mechanisms mentioned above, the proposed MMC is a tracking method that performs satisfactory effects. After finding human faces, we tell the direction of each human face, and research the human-robot interaction between human and robot that is called Joint Attention. We establish joint attention with a human by utilizing both static and dynamic information. As the static information, we extract the edge image of the human face when he/she is gazing at the object. As the dynamic information, the robot uses the optical flow detected when observing a human who is shifting his/her gaze from looking at the robot to looking at another object. The static and dynamic information have complementary characteristics. The static information gives the exact direction of gaze, even though it is difficult to interpret. On the other hand, the dynamic information provides a rough direction but it is easily understandable relationship between the direction of gaze shift and motor output to follow the gaze. We use Support Vector Machine (SVM) for learning model. Utilizing both static and dynamic information acquired from observing a human’s gaze shift enables the robot to efficiently acquire joint attention ability and to naturally interact with the human by SVM. The dynamic information accelerates the learning of joint attention while the static information improves the task performance. From experiment results, the proposed Modified Multi-CAMSHIFT was successfully applied to multiple faces tracking and the development of the Joint Attention.