Learning Behavioral Patterns of Time Series for Video-Surveillance

Abstract

This chapter deals with the problem of learning behaviors of people ac- tivities from (possibly big) sets of visual dynamic data, with a specific reference to video-surveillance applications. The study focuses mainly on devising meaningful data abstractions able to capture the intrinsic nature of the available data, and ap- plying similarity measures appropriate to the specific representations. The methods are selected among the most promising techniques available in the literature and in- clude classical curve fitting, string-based approaches, and hidden Markov models. The analysis considers both supervised and unsupervised settings and is based on a set of loosely labeled data acquired by a real video-surveillance system. The ex- periments highlight different peculiarities of the methods taken into consideration, and the final discussion guides the reader towards the most appropriate choice for a given scenario.