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Our research concentrates on developing shape representations which may be used for the recognition of two-dimensional objects.
Human action recognition research is devoted to extract high-level semantic knowledge from the underlying video or image data, so that the computer has the ...
Jan 10, 2022 · In this paper, using a selective ensemble support vector machine to fuse multimodal features for human action recognition is proposed. The ...
Selective Ensemble Learning based Human Action Recognition Using Fusing Visual Features (2018). First Author: Tang C. Attributed to: National Centre for ...
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The experimental results have demonstrated that the proposed method is simple, fast, and efficient on public datasets in comparison with other action ...
The goal is to select an important discriminating features to recognize the human activities in videos and removing the irrelevant redundant features. The ...
Missing: Selective | Show results with:Selective
Compared with the traditional method, the HAR based on RGB-D has high accuracy and strong robustness. In this paper, using a selective ensemble support vector ...
This study proposes an ensemble learning algorithm (ELA) to perform activity recognition using the signals recorded by smartphone sensors.
This study proposed a lightweight, multi-layer, and hybrid LSTM-GRU model to recognize the human activities using time series sensory data.
Missing: Selective | Show results with:Selective
Apr 3, 2013 · The widely used space-time interest point descriptors are utilized as visual features, and a support vector machine is employed for both audio- ...
Missing: Selective Ensemble