Vehicle detection and speed estimation with PIR sensors

Brian Donovan; Yanning Li; Raphael Stern; Jiming Jiang; Christian Claudel; Daniel Work

doi:10.1145/2737095.2742918

Vehicle detection and speed estimation with PIR sensors

Brian Donovan, Yanning Li, Raphael Stern, Jiming Jiang, Christian Claudel, Daniel Work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

Reliable and accurate traffic sensing is the basis of Intelligent Transportation Systems (ITS), which mitigate traffic mobility and safety issues. To promote vast adoption of ITS technologies, rapid deployment and auto-calibration of traffic sensing systems are critical. Aiming at the development of an advanced traffic sensing system for construction zones, this poster presents our preliminary results for detecting vehicles and estimating traffic speeds by applying signal processing and machine learning techniques using Passive Infrared (PIR) sensor data.

Original language	English (US)
Title of host publication	IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)
Publisher	Association for Computing Machinery, Inc
Pages	370-371
Number of pages	2
ISBN (Electronic)	9781450334754
DOIs	https://doi.org/10.1145/2737095.2742918
State	Published - Apr 13 2015
Externally published	Yes
Event	14th International Symposium on Information Processing in Sensor Networks, IPSN 2015 - Seattle, United States Duration: Apr 13 2015 → Apr 16 2015

Publication series

Name	IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)

Other

Other	14th International Symposium on Information Processing in Sensor Networks, IPSN 2015
Country/Territory	United States
City	Seattle
Period	4/13/15 → 4/16/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access

10.1145/2737095.2742918

OpenUrl availability

Full text

Cite this

Donovan, B., Li, Y., Stern, R., Jiang, J., Claudel, C., & Work, D. (2015). Vehicle detection and speed estimation with PIR sensors. In IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week) (pp. 370-371). (IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)). Association for Computing Machinery, Inc. https://doi.org/10.1145/2737095.2742918

Vehicle detection and speed estimation with PIR sensors. / Donovan, Brian; Li, Yanning; Stern, Raphael et al.
IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week). Association for Computing Machinery, Inc, 2015. p. 370-371 (IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Donovan, B, Li, Y, Stern, R, Jiang, J, Claudel, C & Work, D 2015, Vehicle detection and speed estimation with PIR sensors. in IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week). IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week), Association for Computing Machinery, Inc, pp. 370-371, 14th International Symposium on Information Processing in Sensor Networks, IPSN 2015, Seattle, United States, 4/13/15. https://doi.org/10.1145/2737095.2742918

Donovan B, Li Y, Stern R, Jiang J, Claudel C, Work D. Vehicle detection and speed estimation with PIR sensors. In IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week). Association for Computing Machinery, Inc. 2015. p. 370-371. (IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)). doi: 10.1145/2737095.2742918

Donovan, Brian ; Li, Yanning ; Stern, Raphael et al. / Vehicle detection and speed estimation with PIR sensors. IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week). Association for Computing Machinery, Inc, 2015. pp. 370-371 (IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)).

@inproceedings{f9810e6d6510404da7741eed3334a479,

title = "Vehicle detection and speed estimation with PIR sensors",

abstract = "Reliable and accurate traffic sensing is the basis of Intelligent Transportation Systems (ITS), which mitigate traffic mobility and safety issues. To promote vast adoption of ITS technologies, rapid deployment and auto-calibration of traffic sensing systems are critical. Aiming at the development of an advanced traffic sensing system for construction zones, this poster presents our preliminary results for detecting vehicles and estimating traffic speeds by applying signal processing and machine learning techniques using Passive Infrared (PIR) sensor data.",

author = "Brian Donovan and Yanning Li and Raphael Stern and Jiming Jiang and Christian Claudel and Daniel Work",

year = "2015",

month = apr,

day = "13",

doi = "10.1145/2737095.2742918",

language = "English (US)",

series = "IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)",

publisher = "Association for Computing Machinery, Inc",

pages = "370--371",

booktitle = "IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)",

note = "14th International Symposium on Information Processing in Sensor Networks, IPSN 2015 ; Conference date: 13-04-2015 Through 16-04-2015",

}

TY - GEN

T1 - Vehicle detection and speed estimation with PIR sensors

AU - Donovan, Brian

AU - Li, Yanning

AU - Stern, Raphael

AU - Jiang, Jiming

AU - Claudel, Christian

AU - Work, Daniel

PY - 2015/4/13

Y1 - 2015/4/13

N2 - Reliable and accurate traffic sensing is the basis of Intelligent Transportation Systems (ITS), which mitigate traffic mobility and safety issues. To promote vast adoption of ITS technologies, rapid deployment and auto-calibration of traffic sensing systems are critical. Aiming at the development of an advanced traffic sensing system for construction zones, this poster presents our preliminary results for detecting vehicles and estimating traffic speeds by applying signal processing and machine learning techniques using Passive Infrared (PIR) sensor data.

AB - Reliable and accurate traffic sensing is the basis of Intelligent Transportation Systems (ITS), which mitigate traffic mobility and safety issues. To promote vast adoption of ITS technologies, rapid deployment and auto-calibration of traffic sensing systems are critical. Aiming at the development of an advanced traffic sensing system for construction zones, this poster presents our preliminary results for detecting vehicles and estimating traffic speeds by applying signal processing and machine learning techniques using Passive Infrared (PIR) sensor data.

UR - http://www.scopus.com/inward/record.url?scp=84954094958&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84954094958&partnerID=8YFLogxK

U2 - 10.1145/2737095.2742918

DO - 10.1145/2737095.2742918

M3 - Conference contribution

AN - SCOPUS:84954094958

T3 - IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)

SP - 370

EP - 371

BT - IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)

PB - Association for Computing Machinery, Inc

T2 - 14th International Symposium on Information Processing in Sensor Networks, IPSN 2015

Y2 - 13 April 2015 through 16 April 2015

ER -

Vehicle detection and speed estimation with PIR sensors

Abstract

Publication series

Other

UN SDGs

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this