Human computing and machine understanding of human behavior: a survey

By Maja Pantic, Alex Pentland, Anton Nijholt, Thomas S. Huang. International Conference on Multimodal Interaction, 2006

518 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/9038c1d89c12a933bcc3ceae899f625e3c41a3c2

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In Artificial Intelligence

In Experimental and Cognitive Psychology

In Signal Processing

Citations in the corpus

A Review of Human Activity Recognition Methods (2015, background): “Some representative frames that summarize the main human action classes are depicted in Figure 3.”
Human-Computer Interaction: Overview on State of the Art (2008, unknown): “be improved by 35% when human judges are given access to both face and body modalities together [66].”
Fall detection using ceiling-mounted 3D depth camera (2015, background): “The current research concentrates more on natural settings and moves from generic techniques to specific scenarios and applications (Pantic et al., 2006; Weinland et al., 2011).”
Facial Expression Recognition (2009, background): “pervasive computing, ambient intelligence, and ▶ human computing) will need to develop human-centered ▶ user interfaces that respond readily to naturally occurring, multimodal, human communication [1].”
Facial Expression Recognition via a Boosted Deep Belief Network (2014, background): “Previous Work Extensive efforts have been devoted to recognize facial expressions [19, 31].”
Bridging the Gap between Social Animal and Unsocial Machine: A Survey of Social Signal Processing (2012, unknown): “However, most of the present approaches to machine analysis of human behavior are neither multimodal nor context sensitive nor suitable for handling longer time scales [8], [13], [83], [141].”
Video-Based Human Behavior Understanding: A Survey (2013, background): “The interest in behavior understanding has dramatically increased in recent years, motivated by societal needs that include security [1], natural interfaces [2], gaming [3], affective computing [4], and assisted living [5].”
A Review on Video-Based Human Activity Recognition (2013, background): “Finally, human activity recognition tasks constitute the foundation of human behavior understanding, which requires additional contextual information such as W5+ (who, where, what, when, why, and how) [144].”
A survey of video datasets for human action and activity recognition (2013, background): “A http://dx.doi.org/10.1016/j.cviu.2013.01.013 q This paper has been recommended for acceptance ⇑ Corresponding author.”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, methodology): “By mimicking human intelligence, ML may be deemed to be the most appropriate tool for adapting the network’s structure to the human behavior observed [22], [23].”
Social signal processing: Survey of an emerging domain (2009, background): “Although the importance of social signals in everyday life situations is evident, and in spite of recent advances in machine analysis and synthesis of relevant behavioural cues like gaze exchange, blinks, smiles, head nods, crossed arms, laughter, and similar [137][138], the research efforts in mach”
Fundamentals of physiological computing (2009, background): “As technology develops in this direction, the interaction between users and machines will shift from a master–slave dyad towards a collaborative, symbiotic relationship that requires the computer to extend awareness of the user in real-time (Klein et al., 2004; Pantic et al., 2007).”
On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward (2020, background): “Numerous empirical models have been created to abstract human behavior in robotics and virtual reality (47).”
Video-Based Abnormal Human Behavior Recognition—A Review (2012, background): “, tracking [35], human–computer interactions [36], rehabilitation [37], pedestrian protection sys-”