Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/198368
Title: Vision-Based Positioning for Internet-of-Vehicles
Authors: Kuan-Wen Chen;Chun-Hsin Wang;Xiao Wei;Qiao Liang;Chu-Song Chen;Ming-Hsuan Yang;Yi-Ping Hung
Year: 2017
Publisher: IEEE
Abstract: This paper presents an algorithm for ego-positioning by using a low-cost monocular camera for systems based on the Internet-of-Vehicles. To reduce the computational and memory requirements, as well as the communication load, we tackle the model compression task as a weighted k-cover problem for better preserving the critical structures. For real-world vision-based positioning applications, we consider the issue of large scene changes and introduce a model update algorithm to address this problem. A large positioning data set containing data collected for more than a month, 106 sessions, and 14275 images is constructed. Extensive experimental results show that submeter accuracy can be achieved by the proposed ego-positioning algorithm, which outperforms existing vision-based approaches.
URI: http://localhost/handle/Hannan/198368
volume: 18
issue: 2
More Information: 364,
376
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7506097.pdf3.4 MBAdobe PDF
Title: Vision-Based Positioning for Internet-of-Vehicles
Authors: Kuan-Wen Chen;Chun-Hsin Wang;Xiao Wei;Qiao Liang;Chu-Song Chen;Ming-Hsuan Yang;Yi-Ping Hung
Year: 2017
Publisher: IEEE
Abstract: This paper presents an algorithm for ego-positioning by using a low-cost monocular camera for systems based on the Internet-of-Vehicles. To reduce the computational and memory requirements, as well as the communication load, we tackle the model compression task as a weighted k-cover problem for better preserving the critical structures. For real-world vision-based positioning applications, we consider the issue of large scene changes and introduce a model update algorithm to address this problem. A large positioning data set containing data collected for more than a month, 106 sessions, and 14275 images is constructed. Extensive experimental results show that submeter accuracy can be achieved by the proposed ego-positioning algorithm, which outperforms existing vision-based approaches.
URI: http://localhost/handle/Hannan/198368
volume: 18
issue: 2
More Information: 364,
376
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7506097.pdf3.4 MBAdobe PDF
Title: Vision-Based Positioning for Internet-of-Vehicles
Authors: Kuan-Wen Chen;Chun-Hsin Wang;Xiao Wei;Qiao Liang;Chu-Song Chen;Ming-Hsuan Yang;Yi-Ping Hung
Year: 2017
Publisher: IEEE
Abstract: This paper presents an algorithm for ego-positioning by using a low-cost monocular camera for systems based on the Internet-of-Vehicles. To reduce the computational and memory requirements, as well as the communication load, we tackle the model compression task as a weighted k-cover problem for better preserving the critical structures. For real-world vision-based positioning applications, we consider the issue of large scene changes and introduce a model update algorithm to address this problem. A large positioning data set containing data collected for more than a month, 106 sessions, and 14275 images is constructed. Extensive experimental results show that submeter accuracy can be achieved by the proposed ego-positioning algorithm, which outperforms existing vision-based approaches.
URI: http://localhost/handle/Hannan/198368
volume: 18
issue: 2
More Information: 364,
376
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7506097.pdf3.4 MBAdobe PDF