Human Behavior Analysis from Video Data Using Bag-of-Gestures

Human Behavior Analysis in Uncontrolled Environments can be categorized in two main challenges: 1) Feature extraction and 2) Behavior analysis from a set of corporal language vocabulary. In this work, we present our achievements characterizing some simple behaviors from visual data on different real applications and discuss our plan for future work: low level vocabulary definition from bag-of-gesture units and high level modelling and inference of human behaviors

Multi-modal human gesture recognition combining dynamic programming and probabilistic methods

In this M. Sc. Thesis, we deal with the problem of Human Gesture Recognition using Human Behavior Analysis technologies. In particular, we apply the proposed methodologies in both health care and social applications. In these contexts, gestures are usually performed in a natural way, producing a high variability between the Human Poses that belong to them. This fact makes Human Gesture Recognition a very challenging task, as well as their generalization on developing technologies for Human Behavior Analysis. In order to tackle with the complete framework for Human Gesture Recognition, we split the process in three main goals: Computing multi-modal feature spaces, probabilistic modelling of gestures, and clustering of Human Poses for Sub-Gesture representation. Each of these goals implicitly includes different challenging problems, which are interconnected and faced by three presented approaches: Bag-of-Visual-and-Depth-Words, Probabilistic-Based Dynamic Time Warping, and Sub-Gesture Representation. The methodologies of each of these approaches are explained in detail in the next sections. We have validated the presented approaches on different public and designed data sets, showing high performance and the viability of using our methods for real Human Behavior Analysis systems and applications. Finally, we show a summary of different related applications currently in development, as well as both conclusions and future trends of research

Non-Verbal Communication Analysis in Victim-Offender Mediations

In this paper we present a non-invasive ambient intelligence framework for the semi-automatic analysis of non-verbal communication applied to the restorative justice field. In particular, we propose the use of computer vision and social signal processing technologies in real scenarios of Victim-Offender Mediations, applying feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues from the fields of psychology and observational methodology. We test our methodology on data captured in real world Victim-Offender Mediation sessions in Catalonia in collaboration with the regional government. We define the ground truth based on expert opinions when annotating the observed social responses. Using different state-of-the-art binary classification approaches, our system achieves recognition accuracies of 86% when predicting satisfaction, and 79% when predicting both agreement and receptivity. Applying a regression strategy, we obtain a mean deviation for the predictions between 0.5 and 0.7 in the range [1-5] for the computed social signals.Comment: Please, find the supplementary video material at: http://sunai.uoc.edu/~vponcel/video/VOMSessionSample.mp