Detecting Road Users at Intersections Through Changing Weather Using RGB-Thermal Videos

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Abstract

This paper compares the performance of a watch-dog sys- tem that detects road user actions in urban intersections to a KLT- based tracking system used in traffic surveillance. The two approaches are evaluated on 16 hours of video data captured by RGB and ther- mal cameras under challenging light and weather conditions. On this dataset, the detection performance of right turning vehicles, left turn- ing vehicles, and straight going cyclists are evaluated. Results from both systems show good performance when detecting turning vehicles with a precision of 0.90 and above depending on environmental conditions. The detection performance of cyclists shows that further work on both systems is needed in order to obtain acceptable recall rates.
Original languageEnglish
Title of host publicationAdvances in Visual Computing : 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings, Part I
PublisherSpringer
Publication date23 Dec 2015
Pages741-751
ISBN (Print)978-3-319-27856-8
ISBN (Electronic)978-3-319-27857-5
DOIs
Publication statusPublished - 23 Dec 2015
EventISVC 2015: 11th International Symposium on Visual Computing - Las Vegas, United States
Duration: 14 Dec 201516 Dec 2015

Conference

ConferenceISVC 2015
CountryUnited States
CityLas Vegas
Period14/12/201516/12/2015
SeriesLecture Notes in Computer Science
Volume9474
ISSN0302-9743

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Bahnsen, C., & Moeslund, T. B. (2015). Detecting Road Users at Intersections Through Changing Weather Using RGB-Thermal Videos. In Advances in Visual Computing: 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings, Part I (pp. 741-751). Springer. Lecture Notes in Computer Science, Vol.. 9474 https://doi.org/10.1007/978-3-319-27857-5_66
Bahnsen, Chris ; Moeslund, Thomas B. / Detecting Road Users at Intersections Through Changing Weather Using RGB-Thermal Videos. Advances in Visual Computing: 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings, Part I. Springer, 2015. pp. 741-751 (Lecture Notes in Computer Science, Vol. 9474).
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abstract = "This paper compares the performance of a watch-dog sys- tem that detects road user actions in urban intersections to a KLT- based tracking system used in traffic surveillance. The two approaches are evaluated on 16 hours of video data captured by RGB and ther- mal cameras under challenging light and weather conditions. On this dataset, the detection performance of right turning vehicles, left turn- ing vehicles, and straight going cyclists are evaluated. Results from both systems show good performance when detecting turning vehicles with a precision of 0.90 and above depending on environmental conditions. The detection performance of cyclists shows that further work on both systems is needed in order to obtain acceptable recall rates.",
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Bahnsen, C & Moeslund, TB 2015, Detecting Road Users at Intersections Through Changing Weather Using RGB-Thermal Videos. in Advances in Visual Computing: 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings, Part I. Springer, Lecture Notes in Computer Science, vol. 9474, pp. 741-751, Las Vegas, United States, 14/12/2015. https://doi.org/10.1007/978-3-319-27857-5_66

Detecting Road Users at Intersections Through Changing Weather Using RGB-Thermal Videos. / Bahnsen, Chris; Moeslund, Thomas B.

Advances in Visual Computing: 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings, Part I. Springer, 2015. p. 741-751 (Lecture Notes in Computer Science, Vol. 9474).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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Bahnsen C, Moeslund TB. Detecting Road Users at Intersections Through Changing Weather Using RGB-Thermal Videos. In Advances in Visual Computing: 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings, Part I. Springer. 2015. p. 741-751. (Lecture Notes in Computer Science, Vol. 9474). https://doi.org/10.1007/978-3-319-27857-5_66