Browsing by Author "Tsou, Tai-Yu"

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Accompanist Recognition and Tracking for Intelligent Wheelchairs
(2014)
Wu, Bing-Fei
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Jen, Cheng-Lung
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Tsou, Tai-Yu
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Chen, Po-Yen
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電控工程研究所
Recently, several robotic wheelchairs have been proposed that employ autonomous functions. In designing wheelchairs, it is important to reduce the accompanist load. To provide such a task, the mobile robot needs to recognize and track people. In this paper, we propose to utilize the multisensory data fusion to track a target accompanist. First, the simultaneous localization and map building is achieved by using the laser range finder (LRF) and inertial sensors with the extended Kalman filter recursively. To track the target person robustly, the accompanist, are tracked by fusing laser and vision data. The human objects are detected by LRF, and the identity of accompanist is recognized using a PTZ camera with a pre-defined signature using the speed-up robust features algorithm. The proposed system can adaptively search visual signature and track the accompanist by dynamically zooming the PTZ camera based on LRF detection results to enlarge the range of human following. The experimental results verified and demonstrated the performance of the proposed system.
Adaptive Online Learning for Human Tracking
(2013-01-01)
Wu, Bing-Fei
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Tseng, Pin-Yi
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Jen, Cheng-Lung
;
Tsou, Tai-Yu
;
Hsiao, Kai-Tse
In this work, we present a multiple classifiers system cascades an on-line learning RGB-D appearance model framework in which detection, recognition, and tracking are highly coupled for a wheelchair robot equipped with a Kinect sensor to improve the efficiency of the care assistance and quality of accompanying service. The on-line trained classifiers use the surrounding background as negative examples in the updating which allows the algorithm to choose the most discriminative features between the target and the background, incrementally adjust to the changes in specific tracking environment. Meanwhile, a depth clustering based human detection is proposed to extract human candidates. Accordantly, an on-line learning RGB-D appearance model is cascaded to strengthen the human tracking function by dealing with color, depth and position information from the identified caregiver. Consequently, several experiments have been conducted to demonstrate the effectiveness and feasibility in real world environments.
RGB-D Sensor Based SLAM and Human Tracking with Bayesian Framework for Wheelchair Robots
(2013)
Wu, Bing-Fei
;
Jen, Cheng-Lung
;
Li, Wun-Fang
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Tsou, Tai-Yu
;
Tseng, Pin-Yi
In this paper, we present an approach to visual SLAM and human tracking for a wheelchair robot equipped with a Microsoft Kinect sensor that which is a novel sensing system that captures RGB and depth (RGB-D) images simultaneously. The speeded-up robust feature (SURF) algorithm is employed to provide the robust description of feature for environments and the target person from RGB images. Based on the environmental SURF features, we present the natural landmark based simultaneous localization and mapping with the extended Kalman filter suing RGB-D data. Meanwhile, a depth clustering based human detection is proposed to extract human candidates. Accordantly, the target person tracking is achieved with an online learned RGB-D appearance model by integrating histogram orientation of gradient descriptor, color, depth, and position information from the body of the identified caregiver. Moreover, a fuzzy based controller provides dynamical human following for the wheelchair robot with a desired interval. Consequently, the experimental results demonstrated the effectiveness and feasibility in real world environments.