Human Activity Recognition: A Comparative Evaluation of Spatio-Temporal Descriptors

The study presented in this thesis aims to implement and evaluate three different features for the recognition of human activities (HAR). A feature is a piece of information appropriately extracted from raw data that is relevant for solving a particular task. With HAR is meant that discipline able to automatically recognize the activity carried out by an agent through the collection of data. These systems can be realized by exploiting data of different kinds and coming from different sources. Within this study, it was decided to use visual data from properly recorded videos. Each feature is analyzed and evaluated in every detail and finally implemented in a project that uses the most famous and used computer vision and machine learning libraries. The effectiveness of each is evaluated through two different datasets recognized by the scientific community. Each feature was evaluated not only for its performance but also for the computational cost involved in the extraction. The final analysis is therefore the result of a compromise between results obtained and costs to be incurred.