| 한글제목(Korean Title) |
Crowd Activity Recognition using Optical Flow Orientation Distribution |
| 영문제목(English Title) |
Crowd Activity Recognition using Optical Flow Orientation Distribution |
| 저자(Author) |
Jinpyung Kim
Gyujin Jang
Gyujin Kim
Moon-Hyun Kim
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| 원문수록처(Citation) |
VOL 09 NO. 08 PP. 2948 ~ 2963 (2015. 08) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In the field of computer vision, visual surveillance systems have recently become an important research topic. Growth in this area is being driven by both the increase in the availability of inexpensive computing devices and image sensors as well as the general inefficiency of manual surveillance and monitoring. In particular, the ultimate goal for many visual surveillance systems is to provide automatic activity recognition for events at a given site. A higher level of understanding of these activities requires certain lower-level computer vision tasks to be performed. So in this paper, we propose an intelligent activity recognition model that uses a structure learning method and a classification method. The structure learning method is provided as a K2-learning algorithm that generates Bayesian networks of causal relationships between sensors for a given activity. The statistical characteristics of the sensor values and the topological characteristics of the generated graphs are learned for each activity, and then a neural network is designed to classify the current activity according to the features extracted from the multiple sensor values that have been collected. Finally, the proposed method is implemented and tested by using PETS2013 benchmark data.
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| 키워드(Keyword) |
Structure learning
crowd behavior recognition
histogram of orientation optical-flow
multi-layer perceptron
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| 파일첨부 |
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