Motion Pattern Analysis for Event and Behavior Recognition. Nayak, M, N., Sethi, J, R., Song, B., Roy-Chowdhury, & K, A. In Moeslund, B, T., Sigal, L., Kruger, V., & Hilton, A., editors, Visual Analysis of Humans, pages 289--309. Springer-Verlag, 2011.
![Motion Pattern Analysis for Event and Behavior Recognition [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper doi abstract bibtex
Paper doi abstract bibtex Activity recognition is a field of computer vision which has shown great progress in the past decade. Starting from simple single person activities, research in activity recognition is moving toward more complex scenes involving multiple objects and natural environments. The main challenges in the task include being able to localize and recognize events in a video and deal with the large amount of variation in viewpoint, speed of movement and scale. This chapter gives the reader an overviewof thework that has taken place in activity recognition, especially in the domain of complex activities involving multiple interacting objects.We begin with adescription of thechallenges inactivity recognition and giveabroadoverview of the different approaches. We go into the details of some of the feature descrip- tors and classification strategies commonly recognized as being the state of the art in this field. We then move to more complex recognition systems, discussing the challenges in complex activity recognition and some of the work which has taken place in this respect. Finally, we provide some examples of recent work in complex activity recognition. The ability to recognize complex behaviors involving multiple interacting objects is a very challenging problem and future work needs to study its various aspects of features, recognition strategies, models, robustness issues, and context, to name a few.
@incollection{ Nayak2011,
abstract = {Activity recognition is a field of computer vision which has shown great progress in the past decade. Starting from simple single person activities, research in activity recognition is moving toward more complex scenes involving multiple objects and natural environments. The main challenges in the task include being able to localize and recognize events in a video and deal with the large amount of variation in viewpoint, speed of movement and scale. This chapter gives the reader an overviewof thework that has taken place in activity recognition, especially in the domain of complex activities involving multiple interacting objects.We begin with adescription of thechallenges inactivity recognition and giveabroadoverview of the different approaches. We go into the details of some of the feature descrip- tors and classification strategies commonly recognized as being the state of the art in this field. We then move to more complex recognition systems, discussing the challenges in complex activity recognition and some of the work which has taken place in this respect. Finally, we provide some examples of recent work in complex activity recognition. The ability to recognize complex behaviors involving multiple interacting objects is a very challenging problem and future work needs to study its various aspects of features, recognition strategies, models, robustness issues, and context, to name a few.},
annote = {Working Book Title: Guide to Visual Analysis of Humans: Looking at People
SEE the LINK for BEST version
},
author = {Nayak, Nandita M and Sethi, Ricky J and Song, Bi and Roy-Chowdhury, Amit K},
booktitle = {Visual Analysis of Humans},
doi = {10.1007/978-0-85729-997-0},
editor = {Moeslund, T B and Sigal, L and Kruger, V and Hilton, A},
file = {:C$\backslash$:/Users/rjs/Documents/Mendeley Desktop/Nayak et al/Visual Analysis of Humans/Nayak et al._2011_Motion Pattern Analysis for Event and Behavior Recognition.pdf:pdf},
isbn = {9780857299970},
pages = {289--309},
publisher = {Springer-Verlag},
title = {{Motion Pattern Analysis for Event and Behavior Recognition}},
url = {http://www.ee.ucr.edu/~amitrc/Activity_chapter_nandita.pdf},
year = {2011}
}Downloads: 0
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Starting from simple single person activities, research in activity recognition is moving toward more complex scenes involving multiple objects and natural environments. The main challenges in the task include being able to localize and recognize events in a video and deal with the large amount of variation in viewpoint, speed of movement and scale. This chapter gives the reader an overviewof thework that has taken place in activity recognition, especially in the domain of complex activities involving multiple interacting objects.We begin with adescription of thechallenges inactivity recognition and giveabroadoverview of the different approaches. We go into the details of some of the feature descrip- tors and classification strategies commonly recognized as being the state of the art in this field. We then move to more complex recognition systems, discussing the challenges in complex activity recognition and some of the work which has taken place in this respect. Finally, we provide some examples of recent work in complex activity recognition. The ability to recognize complex behaviors involving multiple interacting objects is a very challenging problem and future work needs to study its various aspects of features, recognition strategies, models, robustness issues, and context, to name a few. -->\n<!-- </div> -->\n<!-- -->\n\n</div>\n","downloads":0,"abstract":"Activity recognition is a field of computer vision which has shown great progress in the past decade. Starting from simple single person activities, research in activity recognition is moving toward more complex scenes involving multiple objects and natural environments. The main challenges in the task include being able to localize and recognize events in a video and deal with the large amount of variation in viewpoint, speed of movement and scale. This chapter gives the reader an overviewof thework that has taken place in activity recognition, especially in the domain of complex activities involving multiple interacting objects.We begin with adescription of thechallenges inactivity recognition and giveabroadoverview of the different approaches. We go into the details of some of the feature descrip- tors and classification strategies commonly recognized as being the state of the art in this field. We then move to more complex recognition systems, discussing the challenges in complex activity recognition and some of the work which has taken place in this respect. Finally, we provide some examples of recent work in complex activity recognition. 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