An Approach to Robust Visual Knife Detection

• Autorzy: Kmiec M. , Glowacz A.
• Języki publikacji: EN

Abstrakty

EN

Computerised monitoring of CCTV images is attracting a lot of attention both from potential end-users seeking to increase the effectiveness of their video surveillance systems and as a popular research topic as new methods and algorithms are being developed. In this paper an approach to detecting knives in images is presented. It is based on the use of Histograms of Oriented Gradients (HOG), feature descriptors invariant to geometric and photometric transformations except for rotation. We introduce a dataset containing images of knives in different backgrounds and in varying lighting conditions and evaluate the performance of an HOG-based SVM classifier. We study the question of creating a detector based on knife blade colour and discuss the use of GPU parallel computing as a method of speeding up the detection process.

Słowa kluczowe

EN

knife detection; dangerous tool detection; histograms of oriented gradients; computerised monitoring; parallel computing; GPU

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2011
• Tom: Vol. 20, No. 2
• Strony: 215--227
• Opis fizyczny: Bibliogr. 11 poz., il., wykr.

Twórcy

autor

Kmiec M.

autor

Glowacz A.

• Department of Automatics AGH University of Science and Technology, Krakow, Poland

Bibliografia

• [1] Paul Viola and Michael Jones. Rapid Object Detection Using a Boosted Cascade of Simple Features. Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Kauai, USA, 2001.
• [2] Hu W., Tan T., Wang L., Maybank S. A Survey on Visual Surveillance of Object Motion and Behaviors, IEEE Transactions on Systems, Man and Cybernetics, vol. 34, no. 3, 2004.
• [3] Navneet Dalai and Bill Triggs. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. II, pages 886-893, June, 2005.
• [4] INSIGMA Project, AGH University of Science and Technology, 2009, http://insigma.kt.agh.edu.pl
• [5] Ram Krishan Kumar. Detecting Pedestrians and Cars in3D, University of North Carolina, 2009.
• [6] Norbert Buch, James Orwell, Sergio A. Velastin. 3D Extended Histogram of Oriented Gradients (3DHOG) for Classification of Road Users in Urban Scenes, Kingston University, UK, 2009.
• [7] Tadeusiewicz R.: How Intelligent Must A System Be for Image Analysis? Preface to book: Kwasnicka H., Jain L. C. (Eds.): Innovations in Intelligent Image Analysis. Studies in Computational Intelligence, vol. 339, Springer Verlag, Berlin, Heidelberg, New York, pp. V X, 2011.
• [8] T. Joachims. Making large-Scale SVM Learning Practical. Advances in Kernel Methods – Support Vector Learning, B. Schlkopf and C. Burges and A. Smola (ed.), MIT-Press, 1999.
• [9] Navneet Dalai. PhD thesis. Finding people in images and videos. Institut National Polytechnique de Grenoble, July 2006.
• [10] H. Schneiderman and T. Kanade. Object detection using the statistics of parts. International Journal of Computer Vision, 56(3):151177, 2004.
• [11] OpenCV library ver. 2.2, 2011, http://sourcefouge.net/projects/opencvlibrary.