Social conflicts appearing in the media are increas ing public awareness about security issues, resulting in a higher
demand of more exhaustive environment monitoring methods.
Automatic video surveillance systems are a powerful assistance to
public and private security agents. Since the arrival of deep learn ing, object detection and classification systems have experienced
a large improvement in both accuracy and versatility. However,
deep learning-based object detection and classification systems
often require expensive GPU-based hardware to work properly.
This paper presents a novel deep learning-based foreground
anomalous object detection system for video streams supplied by
panoramic cameras, specially designed to build power efficient
video surveillance systems. The system optimises the process
of searching for anomalous objects through a new potential
detection generator managed by three different multivariant
homoscedastic distributions. Experimental results obtained after
its deployment in a Jetson TX2 board attest the good performance
of the system, postulating it as a solvent approach to power saving
video surveillance systems.
