Parallel Computational Intelligence-Based Multi-Camera Surveillance System

Article, Other literature type English OPEN
Orts-Escolano, Sergio ; Garcia-Rodriguez, Jose ; Morell, Vicente ; Cazorla, Miguel ; Azorin, Jorge ; Garcia-Chamizo, Juan (2014)
  • Publisher: Multidisciplinary Digital Publishing Institute
  • Journal: Journal of Sensor and Actuator Networks (issn: 2224-2708)
  • Related identifiers: doi: 10.3390/jsan3020095
  • Subject: multi-core | CUDA | T | growing neural gas | camera networks | visual surveillance | Technology | Ciencia de la Computación e Inteligencia Artificial | GPU | Arquitectura y Tecnología de Computadores

In this work, we present a multi-camera surveillance system based on the use of self-organizing neural networks to represent events on video. The system processes several tasks in parallel using GPUs (graphic processor units). It addresses multiple vision tasks at various levels, such as segmentation, representation or characterization, analysis and monitoring of the movement. These features allow the construction of a robust representation of the environment and interpret the behavior of mobile agents in the scene. It is also necessary to integrate the vision module into a global system that operates in a complex environment by receiving images from multiple acquisition devices at video frequency. Offering relevant information to higher level systems, monitoring and making decisions in real time, it must accomplish a set of requirements, such as: time constraints, high availability, robustness, high processing speed and re-configurability. We have built a system able to represent and analyze the motion in video acquired by a multi-camera network and to process multi-source data in parallel on a multi-GPU architecture. This work was partially funded by the Spanish Government DPI2013-40534-R grant and Valencian Government GV/2013/005 and University of Alicante UA GRE11-01 grants.
  • References (40)
    40 references, page 1 of 4

    1. Hu, W.H.W.; Tan, T.T.T.; Wang, L.W.L.; Maybank, S.M.S. A Survey on Visual Surveillance of Object Motion and Behaviors. IEEE Trans. Syst. Man Cybern. Part C. 2004, 34, 334-352.

    2. Velastin, S.A.; Remagnino, P. Intelligent Distributed Video Surveillance Systems; IET Digital Library: London, UK, 2006.

    3. Collins, R.T.; Lipton, A.J.; Kanade, T. Introduction to the Special Section on Video Surveillance. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22, 745-746.

    4. Howarth, R.J.; Buxton, H. Conceptual Descriptions from Monitoring and Watching Image Sequences. Image Vis. Comput. 2000, 18, 105-135.

    5. Hu, W.; Xie, D.; Tan, T. A Hierarchical Self-Organizing Approach for Learning the Patterns of Motion Trajectories. IEEE Trans. Neural Netw. 2004, 15, 135-144.

    6. Tian, Y.; Tan, T.N.; Sun, H.Z. A Novel Robust Algorithm for Real-Time Object Tracking. Acta Autom. Sin. 2002, 28, 851-853.

    7. Wu, Y.; Liu, Q.; Huang, T.S. An Adaptive Self-Organizing Color Segmentation Algorithm with Application to Robust Real-Time Human Hand Localization. In Proceedings of 4th Asian Conference on Computer Vision, Taipei, Taiwan, 8-11 January 2000; pp. 1106-1111.

    8. Howarth, R.J.; Buxton, H. Analogical Representation of Space and Time. Image Vis. Comput. 1992, 10, 467-478.

    9. Brand, M.; Kettnaker, V. Discovery and Segmentation of Activities in Video. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22, 844-851.

    10. Garcia-Rodriguez, J.; Garcia-Chamizo, J.M. Surveillance and Human-Computer Interaction Applications of Self-Growing Models. Appl. Soft Comput. 2011, 11, 4413-4443.

  • Metrics
    No metrics available