Learning behavioral patterns of time series for video-surveillance

This chapter deals with the problem of learning behaviors of people activities 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 applying similarity measures appropriate to the specific representations. The methods are selected among the most promising techniques available in the literature and include 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 experiments 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