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This paper presents a novel approach to detect human fights based on Hidden Markov Model (HMM). We present two HMM models to the problem. The first one is Fight Model, the second is Ordinary Model. According to the motion analysis between people in the scene, features for human behavior have been proposed. Given the observation value of features in time sequence, the probability can be evaluated by...
Human action recognition is a challenging filed in computer vision. In this paper, a novel probabilistic graphical model, called topic-relative conditional random field(TCRF), is firstly proposed. The model is constructed by adding a topic node and using a triangular-chain structure in the top layer of the linear-chain conditional random field(LCRF) to overcome the drawback of independent and identical...
The ability to recognize human actions is a fundamental problem in many areas of robotics research concerned with human-robot interaction or learning from human demonstration. In this paper, we present a new integrated approach to identifying and recognizing actions in human movement sequences and their reproduction in unknown situations. We propose a set of task-space features to construct probabilistic...
The ability to act in a socially-aware way is a key skill for robots that share a space with humans. In this paper we address the problem of socially-aware navigation among people that meets objective criteria such as travel time or path length as well as subjective criteria such as social comfort. Opposed to model-based approaches typically taken in related work, we pose the problem as an unsupervised...
We propose a model-based approach for human body pose recognition from a single-view depth camera. The proposed algorithm applies an articulated cylinder model to detect human pose and track them based on a particle filter without numerous training data or heuristic detectors. To reduce high degrees of freedom, we adopt a hierarchical method that detects torso and limbs successively. Moreover, we...
In this paper we present an original implementation of a compact saliency model for image sensors. The saliency model combines two features: motion and the central fixation bias. Its implementation was designed for low complexity: it relies on compact operators and requires merely about one frame memory. On-the-fly computation allows for low latency processing of "scanline" readout of image...
This paper introduces new objective related knowledge driven product feature definition method in order to achieve better human influence on feature definition in model based product engineering. The proposed method utilizes earlier relevant results by the authors in knowledge communication intensive product modeling and it is devoted as a contribution to solution for some actual problems in current...
Research in Information Retrieval (IR) experienced a paradigm shift from first having too few documents to search from to now having way too many of them. When users have trouble finding relevant documents, they tend to become frustrated and give up searching. Scholars have attempted to reduce instances of search frustration via query expansion, information filtering, and incorporating user feedback...
This paper proposes a method for alignment of human brain magnetic resonance (MR) image sequences in the brain based on a 3D human brain model (triangulated mesh). The brain model is composed of four components, namely, cerebrum, cerebellum, brain stem and pituitary gland which are represented by four different colors. Synthesized image sequences (cross-sections) are extracted from the model at regular...
Psychologists note that humans regularly use categories to simplify and speed up the process of person perception [1]. The influence of categorical thinking on interpersonal expectations is commonly referred to as a stereotype. The ability to bootstrap the process of learning about a newly encountered, unknown person is critical for robots interacting in complex and dynamic social situations. This...
In this doctoral consortium paper I describe the theme of my research, the model-based generation of consistent emotional turn taking behavior in virtual human conversations and the evaluation of this behavior. My goal is to investigate and generate convincing social behavior in embodied conversational agents.
In order to develop computational methods for determining the reaction of biological systems exposed to THz radiation, conventional approaches of numerical dosimetry have to be extended. The penetration depth of electromagnetic fields at THz frequencies is less than 1 mm and thus substantially smaller than for radio frequencies. In addition, the short wavelengths in this frequency range cause the...
The implications of exposure to electromagnetic waves for human health are a subject of rising concern. Numerical simulations using anatomically realistic computational human models have recently been performed for electromagnetic dosimetry simulation. However, most of these computational models are reference (average) models. In this study, we present a shape-deformable human model to which we applied...
Electrostatic discharge or spark may occur before the human touches a metallic object with different electrical potential. The current may stimulate muscle and peripheral nerves. The in-situ electric fields are computed and compared in anatomically based models of adults and child due to contact current from charged human body. Frequency-dependent finite-difference time-domain method was used, in...
The dynamics of collective attention emerging out of individual viewing experiences from public displays appear to be among the most demanding challenges in understanding the mechanisms of self-adaptation of public opinion. In this paper we approach a model of collective attention from observations of the attention of individuals estimated from their efforts expressing interest. Extending on SEEV,...
Spam calls irritate people a lot. Many methods have been proposed to filter spam calls under different backgrounds. Regarding the sociology and psychology as a guide, we tried to propose a reputation model that can work in most backgrounds. In this paper, we proposed a reputation model based on trusted group and interaction scene, and then simulate the system and analysis the results. It appears a...
Currently, there are many models available which can be used to describe a driver's behaviour for a traffic simulation. Despite the number of available formalisms it is our opinion that existing approaches neglect the interplay between the simulation topology and strategic decisions of simulated drivers. Existing models either disregard strategy updates or focus on short-term strategies only. In this...
As one of the most important achievements during the last decade in product lifecycle management (PLM), contextual object definition became main integrating factor in product models. In this paper, the authors introduce their recent results in contextual product modeling. Four aspects of contextual product object definition are discussed and recent related results are introduced. The aspects are contextual...
Advanced Driver Assistant Systems act, by definition in natural, often poorly structured, environments and are supposed to closely interact with human operators. Both, natural environments as well as human behaviour have no inherent metric and can not be modelled/measured in the classical way physically plausibly behaving systems are described.
Word of mouth represents, from historical times, a powerful mechanism employed within human societies for influencing the behavior of their members. Translated into the computational world, the same feedback mechanism preserves or even broadens its impact. This paper presents an intriguing perspective on using reputation systems: reputation is gained by complying with the norms and norms are modified...
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