A vehicular surveillance and sensing system for car security and tracking applications

Lien W. Chen*, Kun Z. Syue, Yu-Chee Tseng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

In this paper, we propose a Vehicular Surveillance and Sensing System (VS3), which targets at car security and tracking applications. VS3 can be triggered by events detected inside or outside a car, such as abnormal air quality, potential burglary, and identification of some target vehicles (such as stolen cars). Via a 3G module, a user can interact with VS3 via multimedia communications. For security applications, we show how VS3 detects an abnormal CO2 level or potential car burglary, notifies the vehicle owner, and then interacts with the owner. For tracking applications, we show how VS3 identifies potential stolen vehicles, transmits reports to the police department, and get neighboring cars involved to cooperatively track suspicious vehicles. This paper demonstrates our current prototype.

Original languageEnglish
Title of host publicationProceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN '10
Pages426-427
Number of pages2
DOIs
StatePublished - 16 Jul 2010
Event9th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2010 - Stockholm, Sweden
Duration: 12 Apr 201016 Apr 2010

Publication series

NameProceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN '10

Conference

Conference9th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2010
CountrySweden
CityStockholm
Period12/04/1016/04/10

Keywords

  • burglarproof
  • surveillance
  • vehicle tracking

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    Chen, L. W., Syue, K. Z., & Tseng, Y-C. (2010). A vehicular surveillance and sensing system for car security and tracking applications. In Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN '10 (pp. 426-427). (Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN '10). https://doi.org/10.1145/1791212.1791288