Browsing by Author "Chou, Cheng-Fu"

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Multiple target tracking in occlusion area with interacting object models in urban environments
(2018-05-01)
Chen, Jiun-Fu
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Wang, Chieh-Chih
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Chou, Cheng-Fu
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資訊工程學系
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Department of Computer Science
Multiple target tracking in crowded urban environments is a daunting task. High crowdedness complicates motion modeling, and occlusion makes tracking difficult as well. Based on the variable-structure multiple -model (VSMM) estimation framework, this paper extends an interacting object tracking (IOT) scheme with occlusion detection and a virtual measurement model for occluded areas. IOT is composed of a scene interaction model and a neighboring object interaction model. The scene interaction model considers the long-term interactions of a moving object and surroundings, and the neighboring object interaction model considers three short-term interactions. With these interacting object models, the motion feature of a moving object can be represented with the weight of each model. A virtual measurement model is proposed to exploit the motion feature with the IOT scheme under occlusion. The proposed approach was validated using a stationary 2D LIDAR. To verify in occlusion, a 3D LIDAR based benchmark system was developed to extract occluded moving segments. The ample experimental results show that the proposed IOT scheme tracks over 57% of occluded moving objects in an urban intersection. (C) 2018 Elsevier B.V. All rights reserved.
Orientation constraints for Wi-Fi SLAM using signal strength gradients
(1970-01-01)
Yen, Hsiao-Chieh
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Wang, Chieh-Chih
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Chou, Cheng-Fu
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電機工程學系
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Department of Electrical and Computer Engineering
We propose the signal strength gradient (SSG) orientation constraints for simultaneous localization and mapping (SLAM) using Wi-Fi received signal strength (RSS) measurements. We show that under certain circumstances, the relative orientation between nearby trajectory segments can be recovered from the cosine similarity between their SSGs. We then show how to obtain trajectory segments and self-consistent SSGs by jointly segmenting Wi-Fi measurements and odometry. Because SSG orientation constraints inevitably contain outliers, we also evaluate the effectiveness of robust SLAM backends on the proposed constraints. Experiments show that Wi-Fi SLAM using the proposed method can correctly estimate orientations given topologically incorrect initialization on trajectories with little to no overlapping sections.
Orientation constraints for Wi-Fi SLAM using signal strength gradients (vol 56, pg 812, 2020)
(2020-09-01)
Yen, Hsiao-Chieh
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Wang, Chieh-Chih
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Chou, Cheng-Fu
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電機工程學系
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Department of Electrical and Computer Engineering
Simultaneous Heterogeneous Sensor Localization, Joint Tracking, and Upper Extremity Modeling for Stroke Rehabilitation
(2020-09-01)
Chen, Jiun-Fu
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Wang, Chieh-Chih
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Wu, Eric Hsiao-Kuang
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Chou, Cheng-Fu
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電機工程學系
In stroke rehabilitation systems and applications, reliability, accuracy, and occlusion should be taken into consideration. Unfortunately, most existing approaches focus primarily on the first two issues. However, during the stroke rehabilitation process, occlusion leads to incorrect judgements even for medical staff. In order to tackle these three important issues simultaneously, we propose a heterogeneous sensor fusion framework composed of an RGB-D camera and a wearable device to consider occlusion and provide robust joint locations for rehabilitation. To fuse multiple sensor measurements when compensating for occlusion, we apply heterogeneous sensor simultaneous localization, tracking, and modeling to estimate the locations of joints and sensors and construct an upper extremity model for occlusion situation. Virtual measurements based on this model are used to estimate the joint's location during occlusion, and a virtual relative orientation technique is applied to relax system limitations regarding orientation. Experimental results using the proposed approach with synthetic data and data collected from ten subjects show a 4.6 cm error on average and about 15 cm error on average during occlusion. This constitutes a more robust approach for stroke patients which takes into account these three important issues.