Action recognition using RGB-D cameras is a popular research topic. Recognising actions in a pose-invariant manner is very challenging due to view changes, posture changes and huge intra-class variations. This study aims to propose a novel pose-invariant action recognition framework based on kinematic features and object context features. Using RGB, depth and skeletal joints, the proposed framework extracts a novel set of pose-invariant motion kinematic features based on 3D scene flow and captures the motion of body parts with respect to the body. The obtained features are converted to a human body centric space that allows partial viewinvariant recognition of actions. The proposed pose-invariant kinematic features are extracted for both foreground (RGB and depth) and skeleton joints and separate classifiers are trained. Bordacount based classifier decision fusion is employed to obtain an action recognition result. For capturing object context features, a convolutional neural network (CNN) classifier is proposed to identify the involved objects. The proposed context features also include temporal information on object interaction and help in obtaining a final action recognition. The proposed framework works even with non-upright human postures and allows simultaneous action recognition for multiple people, which are topics that remain comparatively unresearched. The performance and robustness of the proposed pose-invariant action recognition framework are tested on several benchmark datasets. We also show that the proposed method works in real-time.

Expanding the spectrum of agent social capabilities is an important challenge in agent-based simulation and other domains. While human-like emotionality has been vastly explored in the last 20 years, little research addresses explicit, psychologically believable social situation modeling. Recently, some important elements have been underlined: hybrid connectionist models outside formal ontologies; complex subjective representations linking culture, personality and norms, and so on, but proposed solutions do not provide a formalized structure of a social experience, expressive and well-grounded in psychology. In this paper, we develop a new approach to social situation modeling based on the dramaturgical and dissonance theories. A new component (Dramaturgical Module) is described with implementation used to generate example behavior depicting new social modeling capabilities and a believable representation of the relevant psychological theories. We present a case scenario with a dramaturgical interpretation of dynamic social situations and related cognitive dissonances resulting in a simple and flexible classification. Easily usable in reasoning, planning or affect generation, dramaturgical interpretation is additionally presented here as basis of social affect generation.

This paper presents a semi-automatic method of parameterizing an existing social context cognition model. It discusses benefits of the social context cognition models for example in personality modeling and their key issue that is parametrization. It briefly introduces social context cognition model and describes a new method of its crowd-sourcing-based parametrization. Later, validation is provided, and ability to recreate social context cognition in the provided samples is presented with good generalization for the unknown cases. Finally, model's stability for the continuous stream of dynamic social context input data is shown. Presented system contributes to the believable agent modeling and social simulations by making much needed applications of social context cognition models easier by addressing the so far unsolved troublesome parametrization issues.