Unsupervised Discovery of Normal and Abnormal Activity Patterns in Indoor and Outdoor Environments

In this paper we propose an adaptive system for monitoring indoor and outdoor environments using movement patterns. Our system is able to discover normal and abnormal activity patterns in absence of any prior knowledge. We employ several feature descriptors, by extracting both spatial and temporal cues from trajectories over a spatial grid. Moreover, we improve the initial feature vectors by applying sparse autoencoders, which help at obtaining optimized and compact representations and improved accuracy. Next, activity models are learnt in an unsupervised manner using clustering techniques. The experiments are performed on both indoor and outdoor datasets. The obtained results prove the suitability of the proposed system, achieving an accuracy of over 98% in classifying normal vs. abnormal activity patterns for both scenarios. Furthermore, a semantic interpretation of the most important regions of the scene is obtained without the need of human labels, highlighting the flexibility of our method.