Deep Learning-Based Action Recognition

Dear Colleagues,

Human action recognition (HAR) has gained popularity because it can be used for numerous applications, such as health-care services, video surveillance, and human–computer interaction. The key to good human action recognition is robust human action modeling and feature representation. Conventional shallow learning algorithms such as support vector machine and random forest require the manual extraction of some representative features from large sensory data. However, manual feature extraction requires prior knowledge and will inevitably lose implicit features.

Recently, deep learning has achieved great success in many challenging research areas, such as image classification and object detection. The greatest advantage of deep learning is its ability to automatically learn representative features from large-scale data. Now, more and more researchers have also been applying deep learning to human action recognition. However, many challenging research problems in terms of accuracy, device heterogeneity, scene changes, and others remain unsolved.

This Issue intends to prompt state-of-the-art methods on deep learning for human action recognition. We invite researchers to submit research papers in this Issue on Deep Learning-Based Action Recognition.

Dr. Hyo Jong Lee
Guest Editor

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