Motion Saliency Based Human Action Recognition in RGBD Videos

Human action recognition (HAR) systems need to process large volumes of data posing several challenges including, but not limited to, accurately identifying the actions and classifying them in near real time. Most of the state of the art solutions rely on machine learning techniques where a substantial amount of time is required both for the training and the inference phases. Existing solutions try to reduce the size of the data that is processed by the HAR classifier by constructing a spatio-temporal region of interest (ST-ROI) based on skeleton data, such as the model-based multimodal network (MMNet). However, the performance of these solutions depends on the accuracy of the skeleton data. This paper presents a novel solution to reduce the size of the data that is processed during classification by constructing an ST-ROI using regions identified as motion salient. The results show that the classification accuracy of the proposed solution is comparable to existing solutions such as MMNet but with a training time that is drastically lower.