Human activity classification using spatio-temporal feature relations

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Date

2012

Author

Akpınar, Kutalmış

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This thesis compares the state of the art methods and proposes solutions for human activity classification from video data. Human activity classification is finding the meaning of human activities, which are captured by the video. Classification of human activity is needed in order to improve surveillance video analysis and summarization, video data mining and robot intelligence. This thesis focuses on the classification of low level human activities which are used as an important information source to determine high level activities. In this study, the feature relation histogram based activity description proposed by Ryoo et al. (2009) is implemented and extended. The feature histogram is widely used in feature based approaches; however, the feature relation histogram has the ability to represent the locational information of the features. Our extension defines a new set of relations between the features, which makes the method more effective for action description. Classifications are performed and results are compared using feature histogram, Ryoo’s feature relation histogram and our feature relation histogram using the same datasets and the feature type. Our experiments show that feature relation histogram performs slightly better than the feature histogram, our feature relation histogram is even better than both of the two. Although the difference is not clearly observable in the datasets containing periodic actions, a 12% improvement is observed for the non-periodic action datasets. Our work shows that the spatio-temporal relation represented by our new set of relations is a better way to represent the activity for classification.

Subject Keywords

Human activity recognition., Pattern recognition systems., Video surveillance, Data mining., Sequential pattern mining., Human engineering.

URI

http://etd.lib.metu.edu.tr/upload/12614587/index.pdf
https://hdl.handle.net/11511/21779

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

K. Akpınar, “Human activity classification using spatio-temporal feature relations,” M.S. - Master of Science, Middle East Technical University, 2012.