Monitoring system to detect potential dangerous situations

Witold Wabik

Abstract


Publication contains description of building monitoring system. System has AI module to allow it to identify people on camera images. System detects danger situations in real-time and allows user to analyze recorded events.

Keywords


monitoring; data warehouse; face identification; detecting faces

Full Text:

PDF (Polski)

References


Li S. Z., Jain A. K.: Handbook of face recognition. Springer, 2005.

Viola P., Jones M.: Rapid Object Detection using Boosted Cascade of Simple Features. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2001, Vol. 1, 2001, s. 511÷518.

Turk M., Pentland A.: Face Recognition Using Eigenfaces. IEEE Conference on Computer Vision and Pattern Recognition, Maui, Hawaii 1991.

Castrillon-Santana M., Deniz-Suarez O., Anton-Canalıs L., Lorenzo-Navarro J.: Face and Facial Feature Detection, Evalueation - Performance Evaluation of Public Domain Haar Detectors for Face and Facial Feature Detection. International Conference on Computer Vision Theory and Applications, Vol. 1, 2008.

Ou G., Murphey Y. L.: Multi-class pattern classification using neural network. Journal Pattern Recognition, Vol. 40, No. 1, 2007, s. 4÷18.

Erdem C., Ulukaya S., Karaali A., Erdem A.: Combining Haar Feature and skin color based classifiers for face detection. IEEE International Conference on Acoustics, Speech and Signal Processing, 2011, s. 1497÷1500.

Corviee E., Bremond F.: Combining face detection and people tracking in video sequences. 3rd International Conference on Crime Detection and Prevention, 2009, s. 1÷6.

Jones M., Rehg J.: Statistical color models with application to skin detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999.

Rizon M., Firdaus Hashim M., Saad P.: Face Recognition using Eigenfaces and Neural Networks. American Journal of Applied Sciences 2(6), 2006, s. 1872÷1875.

Cootes T., Taylor C.: Statistical Models of Appearance for Computer Vision. University of Manchester, 2004.




DOI: http://dx.doi.org/10.21936/si2012_v33.n2B.208