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A Survey of Computer Vision-Based Human Motion Capture
- Computer Vision and Image Understanding
, 2001
"... A comprehensive survey of computer vision-based human motion capture literature from the past two decades is presented. The focus is on a general overview based on a taxonomy of system functionalities, broken down into four processes: initialization, tracking, pose estimation, and recognition. Each ..."
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Cited by 515 (14 self)
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A comprehensive survey of computer vision-based human motion capture literature from the past two decades is presented. The focus is on a general overview based on a taxonomy of system functionalities, broken down into four processes: initialization, tracking, pose estimation, and recognition. Each process is discussed and divided into subprocesses and/or categories of methods to provide a reference to describe and compare the more than 130 publications covered by the survey. References are included throughout the paper to exemplify important issues and their relations to the various methods. A number of general assumptions used in this research field are identified and the character of these assumptions indicates that the research field is still in an early stage of development. To evaluate the state of the art, the major application areas are identified and performances are analyzed in light of the methods
Probabilistic Posture Classification for Human-Behavior Analysis
- IEEE Transactions on Systems Man and Cybernetics Part a-Systems and Humans
, 2005
"... Abstract—Computer vision and ubiquitous multimedia access nowadays make feasible the development of a mostly automated system for human-behavior analysis. In this context, our proposal is to analyze human behaviors by classifying the posture of the monitored person and, consequently, detecting corre ..."
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Cited by 46 (10 self)
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Abstract—Computer vision and ubiquitous multimedia access nowadays make feasible the development of a mostly automated system for human-behavior analysis. In this context, our proposal is to analyze human behaviors by classifying the posture of the monitored person and, consequently, detecting corresponding events and alarm situations, like a fall. To this aim, our approach can be divided in two phases: for each frame, the projection histograms (Haritaoglu et al., 1998) of each person are computed and compared with the probabilistic projection maps stored for each posture during the training phase; then, the obtained posture is further validated exploiting the information extracted by a tracking module in order to take into account the reliability of the classification of the first phase. Moreover, the tracking algorithm is used to handle occlusions, making the system particularly robust even in indoors environments. Extensive experimental results demonstrate a promising average accuracy of more than 95 % in correctly classifying human postures, even in the case of challenging conditions. Index Terms—People tracking, posture classification, proba-bilistic projection maps (PPMs). I.
Social Signal Processing: State-of-the-art and future perspectives of an emerging domain
- IN PROCEEDINGS OF THE ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA
, 2008
"... The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next- ..."
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Cited by 27 (7 self)
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The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes aset of recommendations for enabling the development of the next generation of socially-aware computing.
The Analysis-by-Synthesis Approach in Human Motion Capture: A Review
, 1999
"... Human motion capture has become a large research area in recent years. Many different devices have been suggested and/or produced. They are, however, cumbersome for the user to use, especially due to wiring. Therefore computer vision researchers have aimed at a more 'pure' solution to the ..."
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Cited by 3 (0 self)
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Human motion capture has become a large research area in recent years. Many different devices have been suggested and/or produced. They are, however, cumbersome for the user to use, especially due to wiring. Therefore computer vision researchers have aimed at a more 'pure' solution to the motion capture problem, using cameras. When the motion of a human is to be captured by a computer vision system the model-based approach seems to be the preferred one. One way of using a human model is to use the analysis-by-synthesis approach. In this paper the analysis-by-synthesis approach to human motion capture is explained and a review of the different types of data and their level of abstraction is presented. These are edges, silhouettes/contours, stick-figures/joints, motion, and low level data.
Contemporary Challenges for a Social Signal processing
"... This paper provides a short overview of Social Signal Processing. The exploration of how we react to the world and interact with it and each other remains one of the greatest scientific challenges. Latest research trends in cognitive sciences argue that our common view of intelligence is too narrow, ..."
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This paper provides a short overview of Social Signal Processing. The exploration of how we react to the world and interact with it and each other remains one of the greatest scientific challenges. Latest research trends in cognitive sciences argue that our common view of intelligence is too narrow, ignoring a crucial range of abilities that matter immensely for how people do in life. This range of abilities is called social intelligence and includes the ability to express and recognize social signals produced during social interactions like agreement, politeness, empathy, friendliness, conflict, etc., coupled with the ability to manage them in order to get along well with others while winning their cooperation. Social Signal Processing (SSP) is the new research domain that aims at understanding and modeling social interactions (human-science goals), and at providing computers with similar abilities in human-computer interaction scenarios (technological goals). SSP is in its infancy and the journey towards artificial social intelligence and socially-aware computing is still long, the paper outlines its future perspectives and some of its most promising applications.
