Real-Time Reservoir Computing Network-Based Systems for Detection Tasks on Visual Contents

Among the various types of artificial neural networks used for event detection in visual contents, those with the ability of processing temporal information, such as recurrent neural networks, have been proved to be more effective. However, training of such networks is often difficult and time consuming. In this work, we show how Reservoir Computing Networks (RCNs) can be used for detecting purposes on raw images. The applicability of RCNs is illustrated using two example challenges, namely isolated digit handwriting recognition on the MNIST dataset as well as detection of the status of a door using self-developed moving pictures from a surveillance camera. Achieving an error rate of 0.92 percent on MNIST, we show that RCN can be a serious competitor to the state-of-the-art. Moreover, we show how RCNs with their simple and yet robust training procedure can be practically used for real surveillance tasks using very low resolution camera sensors.