کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
530539 869774 2013 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Auto learning temporal atomic actions for activity classification
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Auto learning temporal atomic actions for activity classification
چکیده انگلیسی

In this paper, we present a model for learning atomic actions for complex activities classification. A video sequence is first represented by a collection of visual interest points. Then the model automatically clusters visual words into atomic actions (topics) based on their co-occurrence and temporal proximity in the same activity category using an extension of hierarchical Dirichlet process (HDP) mixture model. Our approach is robust to noisy interest points caused by various conditions because HDP is a generative model. Finally, we use both a Naive Bayesian and a linear SVM classifier for the problem of activity classification. We first use the intermediate result of a synthetic example to demonstrate the superiority of our model, then we apply our model on the complex Olympic Sport 16-class dataset and show that it outperforms other state-of-art methods.


► We model the temporal information for complex activity classification.
► We extend HDP by adding temporal Gaussian model.
► Our model outperforms the state-of-art on the Olympic Sports Dataset.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 46, Issue 7, July 2013, Pages 1789–1798
نویسندگان
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