People recognition in aerial imagery is a complex problem since it involves cameras angles, which are subject to six degrees of freedom. Traditional classifiers used in pattern matching algorithms are not robust enough to deal with such issues. Thus it is necessary to use other techniques such as deep learning methods, e.g. Convolutional Neural Networks (CNN), in order to cope with such a complex problem. However, in spite of providing potentially better recognition results, deep learning methods demand greater processing time, hindering its use on on-board embedded systems of small Unmanned Aerial Vehicles (UAV). This work is a step towards real-time (and possibly on-board) people recognition on aerial imagery. We propose a new approach based on CNN for real-time people recognition on aerial images obtained from cameras attached to small UAVs. This approach combines images from a low-cost thermal camera to detect candidate objects and CNN for the classification task. We have compared our approach with two traditional pattern matching approaches: saliency map detection and cascade classifiers. The obtained results show that, by using CNN, higher rates of correct classification within 95% of precision are obtained. These results have been obtained within real-time processing of 1.08 fps in the worst case on a hardware without any GPU processing.
['Diulhio Candido de Oliveira', 'Marco A. Wehrmeister']
Towards Real-Time People Recognition on Aerial Imagery Using Convolutional Neural Networks