Alle Publikationen
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2017
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(2017): Real‐time gesture–based communication using possibility theory–based hidden Markov model. In: Computational Intelligence 33 (4), S. 843-862. DOI: 10.1111/coin.12116
DOI: https://doi.org/10.1111/coin.12116 Abstract: Exploring correct patterns from low‐frequency time‐series data is challenging. For resolving this problem, the concept of possibility theory–based hidden Markov model (PTBHMM) has been proposed. In this article, all three fundamental problems (evaluation, decoding, and learning) of conventional HMM have been addressed using possibility theory. For handling uncertainty, we have used an axiomatic approach of possibility theory proposed by Zadeh. The time complexity of existing solutions of HMM (forward, backward, Viterbi, and Baum Welch) and proposed possibility‐based solutions has been calculated and compared. From the comparison result, it has been found that PTBHMM has lesser time complexity and hence will be more suitable for real‐time gesture–based communication. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
2015
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(2015): Action database for categorizing and inferring human poses from video sequences. In: Robotics and Autonomous Systems 70, S. 116-125. DOI: 10.1016/j.robot.2015.03.001
DOI: https://doi.org/10.1016/j.robot.2015.03.001 Abstract: One of the difficulties in automated recognition of human activities is classifying a video into a specific action class by selecting among a large number of human actions. Technology for understanding complex and varied human actions is necessary for automated surveillance, sports training, computer games, and human–robot interactions. The difficulty of classification comes from a dearth of datasets of human actions that are manually categorized and suitable for use as training data for designing action classifiers. A marker-based motion capture system enables precise measurement of human actions for the purpose of analysis. This type of capture system has several drawbacks, however; in particular, marker-based systems are expensive, intrusive, and complex to use. Despite this, the intensive use of a motion capture system can provide large datasets of human actions, and the datasets can be used to facilitate handling the variety of actions to be classified. Large datasets of human actions measured by motion capture systems are expected to be suitable for use in classifying video segments into the correct human action category, selecting from among a large number of action categories, and for inferring human postures from video. This paper proposes a new concept for a database of human whole body actions and an application to understanding human actions from video. The database contains action configurations, such as positions of body parts, pose descriptors from silhouette images, a stochastic model encoding each sequence of the pose descriptors, and a regression model for predicting the configuration from the pose descriptor. The action configurations are recorded in advance of use by measuring many human actions with a marker-based motion capture system, and silhouette images are created from these configurations. We tested the action database on action classification tasks and human body posture inference tasks. The experimental results show that the action database is suitable for use in both action classification and posture inference. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
2014
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(2014) : An RGB-D based social behavior interpretation system for a humanoid social robot: 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM): Tehran, Iran: IEEE, S. 185-190
DOI: https://doi.org/10.1109/ICRoM.2014.6990898 Abstract: Humanoid social robots that interact with people need to be capable of interpreting the social behavior of their interaction partners in order to respond in a socially appropriate way. In this paper, we present a social behavior interpretation system that enables a humanoid robot to recognize human social behavior by analyzing communicative signals. The system receives the constructed RGB-D scene from a Kinect sensor, extracts information about body gesture and head pose from the scene using Microsoft Kinect SDK, and recognizes eight human social behaviors using a Hidden Markov Model (HMM). We trained the eight-state HMM with a corpus of 35 recorded human-human interaction scenes. The evaluation of the system shows a weighted average recognition rate of 81% for all states.
Keywords: Accuracy, Angemessen(heit) (von Technik), body gesture, eight-state HMM, Feature extraction, Gesture recognition, head pose, Hidden Markov model, Hidden Markov models, human social behavior, human-human interaction scenes, humanlike robot, humanoid social robot, human-robot interaction, ieee xplore, image colour analysis, image sensors, Joints, Kinect sensor, Microsoft Kinect SDK, pose estimation, RGB-D based social behavior interpretation system, RGB-D scene, Robot sensing systems, robot vision, social behavior interpretation system, social behavior recognition, Vectors, weighted average recognition rate
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