Browsing by Author "Tsai, Richard Yi-Chia"

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Enabling Identity-Aware Tracking via Fusion of Visual and Inertial Features
(2019-01-01)
Tsai, Richard Yi-Chia
;
Ke, Hans Ting-Yuan
;
Lin, Kate Ching-Ju
;
Tseng, Yu-Chee
;
資訊工程學系
Person identification and tracking (PIT) is an essential issue in computer vision and robotic applications. It has long been studied and achieved by technologies such as RFID or face/fingerprint/iris recognition. These approaches, however, have their limitations due to environmental constraints (such as lighting and obstacles) or require close contact to specific devices. Therefore, their recognition accuracy highly depends on use scenarios. In this work, we propose RCU (Robot Catch yoU), an accompanyist robot system that provides follow-me or guide-me services. Such robots are capable of distinguishing users' profiles in front of them and keep tracking a specific target person. We study a more challenging scenario where the target person may be under occlusion from time to time. To enable robust PIT, we develop a data fusion technique that integrates two types of sensors, an RGB-D camera and wearable inertial sensors. Since the data generated by these sensors share common features, we are able to fuse them to achieve identity-aware tracking. Practical issues, such as time synchronization and coordinate calibration, are also addressed. We implement our design on a robotic platform and show that it can track a target person even when no biological feature is captured by the RGB-D camera. Our experimental evaluation shows a recognition rate of 95% and a following rate of 88%.
Eye On You: Fusing Gesture Data from Depth Camera and Inertial Sensors for Person Identification
(2018-01-01)
Chang, Wei-Chun
;
Wu, Cheng-Wei
;
Tsai, Richard Yi-Chia
;
Lin, Kate Ching-Ju
;
Tseng, Yu-Chee
Person identification (PID) is a key issue in many IoT applications. It has long been studied and achieved by technologies such as RFID and face/fingerprint/iris recognition. These approaches, however, have their limitations due to environmental constraints (such as lighting and obstacles) or require close contact to specific devices. Therefore, their recognition rates highly depend on use scenarios. To enable reliable and remote PID, in this work, we present EOY (Eye On You) 1, a data fusion approach that combines two kinds of sensors, a 3D depth camera and wearable sensors embedded with inertial measurement unit (IMU). Since these two kinds of data share common features, we are able to fuse them to conduct PID. Further, the result can be transferred to a mobile platform (such as robot) since we have less constraints on devices. To realize EOY, we develop fusion algorithms to address practical challenges, such as asynchronous timing and coordinate calibration. The experimental evaluation shows that EOY can achieve the recognition rate of 95% and is very robust even in crowded areas.