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Authors: David Jardim 1 ; Luis Nunes 2 and Miguel Sales Dias 3

Affiliations: 1 Microsoft Language and Development Center and Instituto Universitário de Lisboa (ISCTE-IUL), Portugal ; 2 Instituto Universitário de Lisboa (ISCTE-IUL) and IT - Instituto de Telecomunicações, Portugal ; 3 Microsoft Language and Development Center, Instituto Universitário de Lisboa (ISCTE-IUL) and ISTAR-IUL, Portugal

Keyword(s): Human action recognition, Motion analysis, Action detection, Human action prediction.

Abstract: Human activity recognition (HAR) has become one of the most active research topics in image processing and pattern recognition. Detecting specific activities in a live feed or searching in video archives still relies almost completely on human resources. Detecting multiple activities in real-time video feeds is currently performed by assigning multiple analysts to simultaneously watch the same video stream. Manual analysis of video is labour intensive, fatiguing, and error prone. Solving the problem of recognizing human activities from video can lead to improvements in several applications fields like in surveillance systems, human computer interfaces, sports video analysis, digital shopping assistants, video retrieval, gaming and health-care. This area has grown dramatically in the past 10 years, and throughout our research we identified a potentially underexplored sub-area: Action Prediction. What if we could infer the future actions of people from visual input? We propose to expan d the current vision-based activity analysis to a level where it is possible to predict the future actions executed by a subject. We are interested in interactions which can involve a single actor, two humans and/or simple objects. For example try to predict if “a person will cross the street” or “a person will try to steal a hand-bag from another” or were will a tenis-player target the next voley. Using a hierarchical approach we intend to represent high-level human activities that are composed of other simpler activities, which are usually called sub-events which may themselves be decomposable. We expect to develop a system capable of predicting the next action in a sequence initially using offline-learning and then with self-improvement/task specialization in mind, using online-learning. (More)

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Paper citation in several formats:
Jardim, D.; Jardim, D.; Nunes, L. and Dias, M. (2015). Human Activity Recognition and Prediction.In Doctoral Consortium - DCPRAM, (ICPRAM 2015) ISBN , pages 24-32

@conference{dcpram15,
author={David Jardim. and David Jardim. and Luis Nunes. and Miguel Sales Dias.},
title={Human Activity Recognition and Prediction},
booktitle={Doctoral Consortium - DCPRAM, (ICPRAM 2015)},
year={2015},
pages={24-32},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={},
}

TY - CONF

JO - Doctoral Consortium - DCPRAM, (ICPRAM 2015)
TI - Human Activity Recognition and Prediction
SN -
AU - Jardim, D.
AU - Jardim, D.
AU - Nunes, L.
AU - Dias, M.
PY - 2015
SP - 24
EP - 32
DO -

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