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.
Orts-Escolano, S., Garcia-Rodriguez, J., Morell, V., Cazorla, M., Azorin, J., & Garcia-Chamizo, J. M. (2014). Parallel computational intelligence-based multi-camera surveillance system. Journal of Sensor and Actuator Networks, 3(2), 95–112. https://doi.org/10.3390/jsan3020095