کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
536939 870647 2014 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Action recognition via structured codebook construction
ترجمه فارسی عنوان
تشخیص عمل از طریق ساخت کتابچه راهنمای ساخت یافته
کلمات کلیدی
تشخیص عمل، مدل های کیسه ای از کلمات، کدبندی ساختاری برنامه نویسی انعطاف پذیر، اطلاعات متنی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی


• We propose a novel structured codebook construction method for action recognition.
• Structured codebook encodes rich spatial and temporal contextual information.
• The structured codebook is built by exploring statistical properties of elementary actions.
• The novel representation is robust to unwanted background features.
• The proposed method prompts the recognition accuracy.

Bag-of-words models have been widely used to obtain the global representation for action recognition. However, these models ignored the structure information, such as the spatial and temporal contextual information for action representation. In this paper, we propose a novel structured codebook construction method to encode spatial and temporal contextual information among local features for video representation. Given a set of training videos, our method first extracts local motion and appearance features. Next, we encode the spatial and temporal contextual information among local features by constructing correlation matrices for local spatio-temporal features. Then, we discover the common patterns of movements to construct the structured codebook. After that, actions can be represented by a set of sparse coefficients with respect to the structured codebook. Finally, a simple linear SVM classifier is applied to predict the action class based on the action representation. Our method has two main advantages compared to traditional methods. First, our method automatically discovers the mid-level common patterns of movements that capture rich spatial and temporal contextual information. Second, our method is robust to unwanted background local features mainly because most unwanted background local features cannot be sparsely represented by the common patterns and they are treated as residual errors that are not encoded into the action representation. We evaluate the proposed method on two popular benchmarks: KTH action dataset and UCF sports dataset. Experimental results demonstrate the advantages of our structured codebook construction.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing: Image Communication - Volume 29, Issue 4, April 2014, Pages 546–555
نویسندگان
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