On Cropping for Gait Recognition: Does Constant-velocity Locomotion Assumption Improve Gait Recognition Accuracy?

Tappei Okimura, Xiang Li, Chi Xu, Yasushi Yagi

Abstract: Most of gait recognition studies focus on feature extraction and matching steps by using cropped image sequences as inputs. We usually use frame-by-frame tight bounding boxes (TBBs) obtained by pedestrian detection or instance segmentation for cropping. Cropping by the TBB, however, suffers from apparent scale changes within gait period (e.g., apparent height changes between single/double support phases), which may cause a drop in gait recognition accuracy. We therefore propose a method of cropping for gait recognition to better preserve the apparent scale by introducing constant-velocity locomotion (CVL) assumption for a short period (e.g., one second). We derive that a bounding box sequence (BBS) in the 2D image coordinate under CVL assumption in the 3D camera coordinate, is represented by non-linear interpolation between the starting and ending frames without camera calibration parameters. We then estimate BBS parameters (i.e., bounding boxes at the starting and ending frames) by generalized Hough transform with voting from pedestrian region proposals. Experiments with OU-MVLP show that the proposed cropping improves the gait recognition accuracies for both model-based and appearance-based approaches.