کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6853625 | 1437211 | 2016 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Sequential Bag-of-Words model for human action classification
ترجمه فارسی عنوان
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Recently, approaches utilizing spatial-temporal features to form Bag-of-Words (BoWs) models have achieved great success due to their simplicity and effectiveness. But they still have difficulties when distinguishing between actions with high inter-ambiguity. The main reason is that they describe actions by orderless bag of features, and ignore the spatial and temporal structure information of visual words. In order to improve classification performance, we present a novel approach called sequential Bag-of-Words. It captures temporal sequential structure by segmenting the entire action into sub-actions. Meanwhile, we pay more attention to the distinguishing parts of an action by classifying sub-actions separately, which is then employed to vote for the final result. Extensive experiments are conducted on challenging datasets and real scenes to evaluate our method. Concretely, we compare our results to some state-of-the-art classification approaches and confirm the advantages of our approach to distinguish similar actions. Results show that our approach is robust and outperforms most existing BoWs based classification approaches, especially on complex datasets with interactive activities, cluttered backgrounds and inter-class action ambiguities.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: CAAI Transactions on Intelligence Technology - Volume 1, Issue 2, April 2016, Pages 125-136
Journal: CAAI Transactions on Intelligence Technology - Volume 1, Issue 2, April 2016, Pages 125-136
نویسندگان
Hong Liu, Hao Tang, Wei Xiao, ZiYi Guo, Lu Tian, Yuan Gao,