Synthesis Pyramid Pooling: A Strong Pooling Method for Gait Recognition in the Wild

Gait recognition has attracted increasing attention from academia and industry as a human recognition technology from a distance in non-intrusive ways without requiring cooperation. Although advanced methods have achieved impressive success in laboratory scenarios, most of them perform poorly in the wild. Prior arts focus on modifying model structure for better extraction of global temporal and partial spatial representations in gait sequences while the aggregation of global spatial and partial temporal information is overlooked. In this paper, we propose a Synthesis Pyramid Pooling framework, named SPP. With no change to the backbone, SPP uses Global Temporal Pooling operation (TP), Horizontal Spatial Pooling operation (HSP), Global Spatial Pooling operation (SP), and Horizontal Temporal Pooling operation (HTP) to extract both global-partial and spatial-temporal gait information. Besides, we propose an Interval Sampling Strategy (ISS) to effectively extract temporal information in HTP. Extensive experiments show that our method obtains state-of-the-art results on two in-the-wild datasets, i.e. Gait3D and GREW, respectively.