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Efficient Skeleton-Based Action Recognition via Joint-Mapping Strategies
Graph convolutional networks (GCNs) have brought remarkable progress in skeleton-based action recognition. However, high computational cost and large model size make models difficult to be applied in real-world embedded system. Specifically, GCN that is applied in automated surveillance system pre-require models such as pedestrian detection and human pose estimation. Therefore, each model should be computationally lightweight and whole process should be operated in real-time. In this paper, we propose two different joint-mapping modules to reduce the number of joint representations, alleviating a total computational cost and model size. Our models achieve better accuracy-latency trade-off compared to previous state-of-the-arts on two datasets, namely NTU RGB+D and NTU RGB+D 120, demonstrating the suitability for practical applications. Furthermore, we measure the latency of the models by using TensorRT framework to compare the models from a practical perspective.