Gait recognition of camouflaged people based on UAV infrared imaging

Abstract

As a unique behavioral feature that can be obtained at distance, gait plays an essential role in special target surveillance, security prevention, and more. If gait recognition can be deployed on unmanned aerial vehicles (UAVs), it will open novel research perspectives for special operations. However, existing researches are dominated by ground view video recognition in constrained scenes rather than aerial view. In addition, gait recognition in complex environments remains challenging due to the similarity of targets and backgrounds, especially in scenarios where individuals intentionally hide identities through camouflage. To address these problems, we utilize the UAV carrying the infrared camera for gait acquisition and propose a novel gait recognition framework of camouflaged people. Firstly, an infrared gait dataset captured by the UAV flying at different altitudes and perspectives is constructed in this paper, named IR-150. Then, a novel hierarchical framework that integrates the advantages of CNN and Transformer is designed for gait recognition, named CTGait. Specially, depth-wise convolution and residual structures are applied to the local aggregation unit and feed-forward networks to introduce the locality mechanism. By means of diagonal masking and learnable temperature parameters, the attention mechanism is redesigned to enhance the recognition accuracy further. Various experiments prove that the CTGait is superior to the state-of-the-art methods, which achieves an average Rank-1 accuracy of 97.74 % and 82.08 % on the datasets CASIA-C and IR-150, respectively. Tests on the public datasets CASIA-B and GREW demonstrate that the CTGait exhibits significant robustness even in complex scenarios.