Parallel Computational Intelligence-Based Multi-Camera Surveillance System

Journal of Sensor and Actuator Networks

Sergio Orts-Escolano²  Jose Garcia-Rodriguez²  Vicente Morell¹  Miguel Cazorla¹  Jorge Azorin² 

[1] Artificial Intelligence Department, University of Alicante, Po. Box 99, 03080 Alicante, Spain; E-Mails:;Computer Technology Department, University of Alicante, Po. Box 99, 03080 Alicante, Spain; E-Mails:

关键词: growing neural gas;    camera networks;    visual surveillance;    GPU;    CUDA;    multi-core;   

DOI  :  10.3390/jsan3020095

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.