A Dataset and Method for Gait Recognition with Unmanned Aerial Vehicless

Abstract

Gait is a promising behavioral biometric trait that can be used to identify persons at a distance. Thanks to its advantages of long working distances, low requirements for data quality, and no requirement for cooperation, gait recognition is quite suitable for deployment with unmanned aerial vehicles (UAVs). However, existing works all consider gait recognition in ground-view videos rather than aerial videos. To fill this gap, this paper for the first time constructs a dataset of both UAV-view and ground-view gait data, namely UAV-Gait, which contains 9,898 gait sequences of 202 individuals captured by one DJI consumer UAV flying at altitudes of 10, 20, and 30 meters as well as by five cameras fixed on the ground. Moreover, this paper proposes a graph convolution based part feature pooling method to improve the robustness of extracted gait features to large view changes, especially to pitch rotations, that are common in UAV-captured images. Evaluation experiments on the UAV-Gait dataset prove that gait recognition with UAVs is very challenging and the proposed method can effectively increase the accuracy of gait recognition in aerial images.