Ray-Tracing-Based Event Detection and 3D Visualization for Automated Video Surveillance System

• Autorzy: Przybyło J. , Jabłoński M.

Identyfikatory

DOI

10.1515/ipc-2015-0032

• Języki publikacji: EN

Abstrakty

EN

Automated and intelligent video surveillance systems play important role in current home care and facilities security applications. Among many research problems is graphical visualization of semantic messages to the human operator that he can percept information in more natural way. The other essential research question is how to recognize 3D objects and their state on the monitored scene only from their views (2D images from the camera). In this paper we continue our previous work on data fusion in visualization of 3D scene semantic model and propose to recognize events and states of scene objects under surveillance in an automatic way using feedback provided by the renderer. We developed ray-tracing based visualization for surveillance system, that is capable of recognizing object’s state and at the same time present relevant information to the human operator.

Słowa kluczowe

EN

video surveillance systems; 2D and 3D visualization; RGB images

• Wydawca: Instytut Telekomunikacji i Informatyki Uniwersytetu Technologiczno-Przyrodniczego w Bydgoszczy
• Czasopismo: Image Processing & Communications
• Rocznik: 2015
• Tom: Vol. 20, no. 2
• Strony: 29--39
• Opis fizyczny: Bibliogr. 17 poz., rys., tab.

Twórcy

autor

Przybyło J.

• AGH University of Science and Technology, Department of Automatics and Bioengineering Mickiewicza Ave., 30, 30-059 Kraków, Poland

autor

Jabłoński M.

• AGH University of Science and Technology, Department of Automatics and Bioengineering Mickiewicza Ave., 30, 30-059 Kraków, Poland

Bibliografia

• [1] Augustyniak, P., Smoleń, M., Mikrut, Z., Kańtoch, E. (2014). Seamless tracing of human behavior using complementary wearable and house-embedded sensors. Sensors, 14(5), 7831-7856
• [2] Bouguet, J.Y. (2004). Camera calibration toolbox for matlab
• [3] Chmiel, W., Kwiecień, J., Mikrut, Z. (2012). Realization of scenarios for video surveillance. Image Processing & Communications, 17(4), 231-240
• [4] Gorodnichy, D., Mungham, T. (2008). Automated video surveillance: challenges and solutions. ACE Surveillance (Annotated Critical Evidence) case study. In NATO SET-125 Symposium” Sensor and Technology for Defence against Terrorism”, Mainheim
• [5] Heikkila, J., Silvén, O. (1997). A four-step camera calibration procedure with implicit image correction. In Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on (pp. 1106-1112). IEEE
• [6] Hsu, G.S., Loc, T.T., Chung, S.L. (2012). A comparison study on appearance-based object recognition. In Pattern Recognition (ICPR), 2012 21st International Conference on (pp. 3500-3503). IEEE
• [7] Jabłoński, M. (2013). Data fusion in web-based visualization of 3D scene semantic model. Image Processing & Communications, 18(2-3), 61-70
• [8] Keselman, Y., Dickinson, S. (2001). Bridging the representation gap between models and exemplars. In Proceedings, IEEE Workshop on Models versus Exemplars in Computer Vision
• [9] Kryjak, T., Gorgoń, M. (2011). Real-time implementation of moving object detection in video surveillance systems using FPGA. Computer Science, 12, 149-162
• [10] Leff, A., Rayfield, J.T. (2001). Web-application development using the model/view/controller design pattern. In Enterprise Distributed Object Computing Conference, 2001. EDOC’01. Proceedings. Fifth IEEE International (pp. 118-127). IEEE
• [11] Lepetit, V., Moreno-Noguer, F., Fua, P. (2009). Epnp: An accurate o (n) solution to the pnp problem. International journal of computer vision, 81(2), 155-166
• [12] Megret, R., Szolgay, D., Benois-Pineau, J., Joly, P., Pinquier, J., Dartigues, J.F., Helmer, C. (2008). Wearable video monitoring of people with age dementia: Video indexing at the service of helthcare. In International Workshop on Content-Based Multimedia Indexing, CBMI 2008 (pp. 101-108)
• [13] Quilez, I. (2009). Rendering worlds with two triangles. http://www.iquilezles.org/www/material/nvscene2008/rwwtt.pdf
• [14] Rabiner, L., Juang, B.H. (1993). Fundamentals of speech recognition. Prentice-Hall, Inc., Upper Saddle River, NJ, USA
• [15] Stauffer, C., Grimson, W.E.L. (1999). Adaptive background mixture models for real-time tracking. In Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on. (Vol. 2). IEEE
• [16] Tomczak, L.J. (2012). GPU Ray Marching of Distance Fields. Technical University of Denmark
• [17] Zhang, Z. (2000). A flexible new technique for camera calibration. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(11), 1330-1334