Context-Aware Faster RCNN for CSI-Based Human Action Perception

With the widespread deployment of commercial wireless devices, researchers begin to focus on device-free sensing tasks. In the field of action perception, existing WiFi-based sensing works mostly follow the framework in which action instances of channel state information (CSI) are first extracted and then classified. As for the part of human action detection, a majority of works adopt threshold based sliding window or frame-by-frame detection methods. However, it is hard for the former approach to set a reasonable threshold for all samples. As for the latter, it costs a relatively substantial amount of labor to label each moment of the time sequences. In order to overcome the above problems, we design an end-to-end context-aware faster region-based convolutional neural networks (RCNN) framework named Wisense to simultaneously detect the temporal boundaries as well as classify the actions. More specifically, Wisense consists of backbone net, region proposal net (RPN), pooling layer, and the prediction net, which directly regresses the action location along the time axis and classifies the action types. For the sake of wireless signal temporal detection, we transform the input into 1-D feature map and extract multiscale 1-D anchors. Besides, in order to sufficiently mine the context information, we extend the boundaries of region proposals and further establish the temporal pyramid features. Experimental results conducted in three indoor scenes validate the effectiveness of our proposed Wisense.