Pedestrian Attitude Estimation and Recognition Algorithm Based on RF Data

Abstract

Pedestrian detection plays a vital role in the estimation of human posture, especially the pedestrian detector, which provides the position information of human body in the image. This paper proposes a pedestrian gesture recognition algorithm using radio frequency data based on RFID technology. Its goal is to serve as a starting point for research in the field of human gesture recognition. The position of pedestrians in the input picture can be adaptively adjusted by learning the transformation parameters of samples during network training, and the local features of each block can be trained by multiple cross entropy functions at the network’s end, allowing the sample local area to be fully utilized as the network’s training. More accurate DFL and attitude feature recognition can be achieved using the measured received signal strength information and channel state information. The pedestrian gesture recognition algorithm based on RF data proposed in this paper improves pedestrian recognition accuracy, especially in noisy environments, such as fuzzy or occlusion, according to experimental results.