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With rapid increase of number of vehicles on roads it is necessary to maintain close monitoring of traffic. For this purpose many surveillance cameras are placed along roads and on crossroads, creating a huge communication load between the cameras and the monitoring center. Therefore, the data needs to be processed on site and transferred to the monitoring centers in form of metadata or as a set of selected images. For this purpose it is necessary to detect events of interest already on the camera side, which implies using smart cameras as visual sensors. In this paper we propose a method for tracking of vehicles and analysis of vehicle trajectories to detect different traffic events. Kalman filtering is used for tracking, combining foreground and optical flow measurements. Obtained vehicle trajectories are used to detect different traffic events. Every new trajectory is compared with collection of normal routes and clustered accordingly. If the observed trajectory differs from all normal routes more than a predefined threshold, it is marked as abnormal and the alarm is raised. The system was developed and tested on Texas Instruments OMAP platform. Testing was done on four different locations, two locations in the city and two locations on the open road.
Keywords
Kalman filter, segmentation, analysis of vehicle trajectories, tracking, optical flow, smart video cameras, FEATURES, TRACKING

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MLA
Macesic, Marko, et al. “Real-Time Detection of Traffic Events Using Smart Cameras.” INTELLIGENT ROBOTS AND COMPUTER VISION XXIX: ALGORITHMS AND TECHNIQUES, edited by J Roning and DP Casasent, vol. 8301, SPIE, 2012, doi:10.1117/12.909461.
APA
Macesic, M., Jelača, V., Niño Castañeda, J., Prodanovic, N., Panic, M., Pizurica, A., … Philips, W. (2012). Real-time detection of traffic events using smart cameras. In J. Roning & D. Casasent (Eds.), INTELLIGENT ROBOTS AND COMPUTER VISION XXIX: ALGORITHMS AND TECHNIQUES (Vol. 8301). https://doi.org/10.1117/12.909461
Chicago author-date
Macesic, Marko, Vedran Jelača, Jorge Niño Castañeda, Nenad Prodanovic, Marko Panic, Aleksandra Pizurica, Vladimir Crnojevic, and Wilfried Philips. 2012. “Real-Time Detection of Traffic Events Using Smart Cameras.” In INTELLIGENT ROBOTS AND COMPUTER VISION XXIX: ALGORITHMS AND TECHNIQUES, edited by J Roning and DP Casasent. Vol. 8301. Bellingham, WA, USA: SPIE. https://doi.org/10.1117/12.909461.
Chicago author-date (all authors)
Macesic, Marko, Vedran Jelača, Jorge Niño Castañeda, Nenad Prodanovic, Marko Panic, Aleksandra Pizurica, Vladimir Crnojevic, and Wilfried Philips. 2012. “Real-Time Detection of Traffic Events Using Smart Cameras.” In INTELLIGENT ROBOTS AND COMPUTER VISION XXIX: ALGORITHMS AND TECHNIQUES, ed by. J Roning and DP Casasent. Vol. 8301. Bellingham, WA, USA: SPIE. doi:10.1117/12.909461.
Vancouver
1.
Macesic M, Jelača V, Niño Castañeda J, Prodanovic N, Panic M, Pizurica A, et al. Real-time detection of traffic events using smart cameras. In: Roning J, Casasent D, editors. INTELLIGENT ROBOTS AND COMPUTER VISION XXIX: ALGORITHMS AND TECHNIQUES. Bellingham, WA, USA: SPIE; 2012.
IEEE
[1]
M. Macesic et al., “Real-time detection of traffic events using smart cameras,” in INTELLIGENT ROBOTS AND COMPUTER VISION XXIX: ALGORITHMS AND TECHNIQUES, Burlingame, CA, USA, 2012, vol. 8301.
@inproceedings{2027417,
  abstract     = {{With rapid increase of number of vehicles on roads it is necessary to maintain close monitoring of traffic. For this purpose many surveillance cameras are placed along roads and on crossroads, creating a huge communication load between the cameras and the monitoring center. Therefore, the data needs to be processed on site and transferred to the monitoring centers in form of metadata or as a set of selected images. For this purpose it is necessary to detect events of interest already on the camera side, which implies using smart cameras as visual sensors. In this paper we propose a method for tracking of vehicles and analysis of vehicle trajectories to detect different traffic events. Kalman filtering is used for tracking, combining foreground and optical flow measurements. Obtained vehicle trajectories are used to detect different traffic events. Every new trajectory is compared with collection of normal routes and clustered accordingly. If the observed trajectory differs from all normal routes more than a predefined threshold, it is marked as abnormal and the alarm is raised. The system was developed and tested on Texas Instruments OMAP platform. Testing was done on four different locations, two locations in the city and two locations on the open road.}},
  articleno    = {{83010E}},
  author       = {{Macesic, Marko and Jelača, Vedran and Niño Castañeda, Jorge and Prodanovic, Nenad and Panic, Marko and Pizurica, Aleksandra and Crnojevic, Vladimir and Philips, Wilfried}},
  booktitle    = {{INTELLIGENT ROBOTS AND COMPUTER VISION XXIX: ALGORITHMS AND TECHNIQUES}},
  editor       = {{Roning, J and Casasent, DP}},
  isbn         = {{9780819489487}},
  issn         = {{0277-786X}},
  keywords     = {{Kalman filter,segmentation,analysis of vehicle trajectories,tracking,optical flow,smart video cameras,FEATURES,TRACKING}},
  language     = {{eng}},
  location     = {{Burlingame, CA, USA}},
  pages        = {{11}},
  publisher    = {{SPIE}},
  title        = {{Real-time detection of traffic events using smart cameras}},
  url          = {{http://doi.org/10.1117/12.909461}},
  volume       = {{8301}},
  year         = {{2012}},
}

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