Context information for human behavior analysis and prediction

Abstract

This work is placed in the context of computer vision and ubiquitous multimedia access. It deals with the development of an automated system for human behavior analysis and prediction using context features as a representative descriptor of human posture. In our proposed method, an action is composed of a series of features over time. Therefore, time sequential images expressing human action are transformed into a feature vector sequence. Then the feature is transformed into symbol sequence. For that purpose, we design a posture codebook, which contains representative features of each action type and define distances to measure similarity between feature vectors. The system is also able to predict next performed motion. This prediction helps to evaluate and choose current action to show. © Springer-Verlag Berlin Heidelberg 2007.