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Vehicle Detection with Self-Training for Adaptative Video Processing Embedded Platform

Abstrakt

Traffic monitoring from closed-circuit television (CCTV) cameras on embedded systems is the subject of the performed experiments. Solving this problem encounters difficulties related to the hardware limitations, and possible camera placement in various positions which affects the system performance. To satisfy the hardware requirements, vehicle detection is performed using a lightweight Convolutional Neural Network (CNN), named SqueezeDet, while, for tracking, the Simple Online and Realtime Tracking (SORT) algorithm is applied, allowing for real-time processing on an NVIDIA Jetson Tx2. To allow for adaptation of the system to the deployment environment, a procedure was implemented leading to generating labels in an unsupervised manner with the help of background modelling and the tracking algorithm. The acquired labels are further used for fine-tuning the model, resulting in a meaningful increase in the traffic estimation accuracy, and moreover, adding only minimal human effort to the process allows for further accuracy improvement. The proposed methods, and the results of experiments organised under real-world test conditions are presented in the paper.

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Wersja publikacji
Accepted albo Published Version
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/app10175763
Licencja
Creative Commons: CC-BY otwiera się w nowej karcie

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Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
Applied Sciences-Basel nr 10,
ISSN: 2076-3417
Język:
angielski
Rok wydania:
2020
Opis bibliograficzny:
Cygert S., Czyżewski A.: Vehicle Detection with Self-Training for Adaptative Video Processing Embedded Platform// Applied Sciences-Basel -Vol. 10,iss. 17 (2020), s.5763-
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/app10175763
Źródła finansowania:
Weryfikacja:
Politechnika Gdańska

wyświetlono 135 razy

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