Layered representations for human activity recognition

By Nuria Oliver, Eric Horvitz, Ashutosh Garg. Proceedings. Fourth IEEE International Conference on Multimodal Interfaces, 2002

364 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/249eea3b0c2e553695c026e2f16ab1a01552332c

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

A survey on activity recognition and behavior understanding in video surveillance (2013, background): “[128] developed layered hidden Markov models (LHMMs), to model a real-time office activities.”
Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors (2005, neutral): “In particular, researchers have used GPS readings to infer walking, driving and bus riding behaviors [23], laser range finders to learn motion paths in a home [8], and combinations of audio and video to recognize behavior in an office environment [22] and interactions between individuals [12].”
A Systematic Review of Hidden Markov Models and Their Applications (2020, background): “Oliver et al. [120] represented humans activity from real-time streams of video, computer interaction and acoustic with LHMM.”
Activity recognition and abnormality detection with the switching hidden semi-Markov model (2005, methodology): “Examples of these models include the Abstract HMM [2], the Hierarchical HMM [3, 8, 1], and the Layered HMM [18].”
Segmenting Motion Capture Data into Distinct Behaviors (2004, methodology): “In the vision community, model-based approaches to recognition, tracking, and segmentation of high-level behaviors are widely used (e.g., [9], [21]).”
Advanced internet of things for personalised healthcare systems: A survey (2017, methodology): “Typical image sensor related IoT enabled PHS cases[100] include SenseCam, Sony Xperia eye, etc.”
a CAPpella: programming by demonstration of context-aware applications (2004, background): “Recognizing human activity is a rapidly growing subfield and has become more prominent in the HCI community of late [10, 19 ,] While most research focuses on techniques for programmers to build useful recognizers, there are a number of systems that investigate the idea of putting a human “in the loo”
COSAR: hybrid reasoning for context-aware activity recognition (2011, background): “A research direction consists in the use of cameras with the help of sound, image and scene recognition software (see, e.g., [5, 21 , 28]).”
Understanding Video Events: A Survey of Methods for Automatic Interpretation of Semantic Occurrences in Video (2009, methodology): “[85] use a layered HMM (LHMM) event model in the event domain of office surveillance.”
Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model (2005, methodology): “In [14], Oliver et al. use the LHMM for recognizing highlevel activities based on real-time streams of signals from multiple sources of sensors.”