Integrating Gaussian mixture model and dilated residual network for action recognition in videos

Online learning has become one of the most important learning styles, yet with the need of supervisors to consistently keep the learners motivated and on-task. Some learners could be supervised by outer factors, and distance learners have to be motivated by themselves. However, online learning engagement is hardly to be assessed by supervisors in real time. With the rapid development of information technology, it is able to remedy the above problem by using intelligent video surveillance techniques. In this paper, we propose a novel framework of learning engagement assessment which introduces facial expression recognition to timely acquire the emotional changes of the learners. Moreover, a new facial expression recognition method is proposed based on domain adaptation, which is suitable for the MOOC scenario. The experiments show the effectiveness of our proposed framework on assessing learners’ learning engagement. The comparisons with the state-of-the-art methods also demonstrate the superiority of our proposed facial emotion recognition method.

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Adequately hierarchical patterns based on pairwise regions

Nguyen,

Bouchara

2024

Multimedia Systems

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Integrating Gaussian mixture model and dilated residual network for action recognition in videos

Cited by 5 publications

References 19 publications

Few-shot imbalanced classification based on data augmentation

Few-shot imbalanced classification based on data augmentation

Assessing learning engagement based on facial expression recognition in MOOC’s scenario

Adequately hierarchical patterns based on pairwise regions

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