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Deep Reinforcement Learning in Human Activity Recognition: A Survey
  • Bahareh Nikpour ,
  • Dimitrios Sinodinos ,
  • Narges Armanfard
Bahareh Nikpour
McGill university, McGill university, McGill university

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Dimitrios Sinodinos
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Narges Armanfard
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Human activity recognition is a popular research field in computer vision that has already been widely studied. However, it is still an active research field since it plays an important role in many current and emerging real world intelligent systems, like visual surveillance and human-computer interaction. Deep Reinforcement Learning (DRL) has recently been employed to address the activity recognition problem with various purposes, such as finding attention in video data or obtaining the best network structure. DRL-based human activity recognition has only been around for a short time, and it is a challenging, novel field[ of study. Therefore, to facilitate further research in this field, we have constructed a comprehensive survey on activity recognition methods that incorporate deep reinforcement learning. Towards the end of this survey, we summarize key challenges and open problems in this area that can be addressed by researchers in the future.