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Low-Cost Scalable Home Video Surveillance System

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Automated and intelligent video processing and analysis systems are becoming increasingly popular in video surveillance. Such systems must meet a number of requirements, such as threat detection and real-time video recording. Furthermore, they cannot be expensive and must not consume too much energy because they have to operate continuously. The work presented here focuses on building a home video surveillance system matching the household budget and possibly making use of hardware available in the house. Also, it must provide basic functionality (such as video recording and detecting threats) all the time, and allow for a more in-depth analysis when more computing power be available.
Twórcy
autor
  • AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Automatics and Bioengineering, Al. Mickiewicza 30, 30-059 Krakow
  • AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Automatics and Bioengineering, Al. Mickiewicza 30, 30-059 Krakow
autor
  • AGH University of Science and Technology, Faculty of Computing Science, Electronics and Telecom-munications, Department of Electronics, Al. Mickiewicza 30, 30-059 Krakow
Bibliografia
  • [1] videoInput. (2015). http://www.muonics.net/school/spring05/videoInput/ (last access: March 2015)
  • [2] Bouwmans, T., Baf, F.E., Vachon, V. (2008). Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey, Recent Patents on Computer Science, 1, 219-237
  • [3] Bublinski, Z., et al. (2011). System inteligentnego monitoringu przestrzeni i obiektów szczególnego znaczenia SIMPOZ, PAR Pomiary Automatyka Robotyka, R.15(12), 69-76
  • [4] Chmiel, W., et al. (2013). Realization of scenarios for video surveillance, Image Processing & Communications, 17(4), 231-240
  • [5] Dalal, N., Triggs, B. (2005). Histograms of oriented gradients for human detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, 886 - 893
  • [6] Devasena, C.L., et al. (2011) Video Surveillance Systems - A Survey, IJCSI International Journal of Computer Science Issues, 8(4), 1694-0814
  • [7] Genovese, M., Napoli, E. (2013). ASIC and FPGA Implementation of the Gaussian Mixture Model Algorithm for real-time segmentation of High Definition video, IEEE Transactions On Very Large Scale Integration (VLSI) Systems
  • [8] Jablonski, M., Przybylo, J. (2014). Evaluation Of Mog Video Segmentation On Gpu-Based Hpc System, Computing and Informatics (to appear)
  • [9] Kryjak, T., Komorkiewicz, M., Gorgon, M. (2011). Implementation of a background generation algorithm with moving object detection and shadow suppressing in Spartan 6 series FPGA devices, Automatyka, AGH UWND, 15(3), 197-217
  • [10] Przybylo, J. (2013). Object detection and tracking for low-cost video surveillance system, Image Processing & Communications, 18(2-3), 91-99
  • [11] Stauffer, C., Grimson, W. (1999). Adaptive background mixture models for real-time tracking, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 246-252
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0da2dd68-86c2-417e-91ac-2b8d3186410e
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