Anomalous human behavior detection using a network of RGB-D sensors

The detection of anomalous behaviors of people in indoor environments is an important topic in surveillance applications, especially when low cost solutions are necessary in contexts such as long corridors of public buildings, where standard cameras with long camera view would be either ineffective or costly to implement. This paper proposes a network of low cost RGB-D sensors with no overlapping fields-of-view, capable of identifying anomalous behaviors with respect a pre-learned normal one. A 3D trajectory analysis is carried out by comparing three different classifiers (SVM, neural networks and k-nearest neighbors). The results on real experiments prove the effectiveness of the proposed approach both in terms of performances and of real time application