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Machine recognition of human activities: A survey (2008)
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BibTeX
@MISC{Turaga08machinerecognition,
author = {Pavan Turaga and Rama Chellappa and V. S. Subrahmanian and Octavian Udrea},
title = {Machine recognition of human activities: A survey },
year = {2008}
}
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Abstract
The past decade has witnessed a rapid proliferation of video cameras in all walks of life and has resulted in a tremendous explosion of video content. Several applications such as content-based video annotation and retrieval, highlight extraction and video summarization require recognition of the activities occurring in the video. The analysis of human activities in videos is an area with increasingly important consequences from security and surveillance to entertainment and personal archiving. Several challenges at various levels of processing—robustness against errors in low-level processing, view and rate-invariant representations at midlevel processing and semantic representation of human activities at higher level processing—make this problem hard to solve. In this review paper, we present a comprehensive survey of efforts in the past couple of decades to address the problems of representation, recognition, and learning of human activities from video and related applications. We discuss the problem at two major levels of complexity: 1) “actions ” and 2) “activities. ” “Actions ” are characterized by simple motion patterns typically executed by a single human. “Activities ” are more complex and involve coordinated actions among a small number of humans. We will discuss several approaches and classify them according to their ability to handle varying degrees of complexity as interpreted above. We begin with a discussion of approaches to model the simplest of action classes known as atomic or primitive actions that do not require sophisticated dynamical modeling. Then, methods to model actions with more complex dynamics are discussed. The discussion then leads naturally to methods for higher level representation of complex activities.
Keyphrases
human activity machine recognition several application review paper primitive action major level simple motion pattern several challenge single human several approach personal archiving semantic representation low-level processing action class midlevel processing rapid proliferation related application past couple video content level representation video camera small number tremendous explosion various level video summarization complex dynamic content-based video annotation sophisticated dynamical modeling comprehensive survey complex activity important consequence highlight extraction rate-invariant representation past decade
