Automatic Estimation of Pedestrian Gait Features using a single camera recording: Algorithm and Statistical Analysis for Gender Difference and Obstacle Interactions

Abstract

The pedestrian gait features - body sway frequency, amplitude, stride length, and speed, along with pedestrian personal space and directional bias, are important parameters to be used in different pedestrian dynamics studies. Gait feature measurements are paramount for wide-ranging applications, varying from the medical field to the design of bridges. Personal space and choice of direction (directional bias) play important roles during crowd simulations. In this study, we formulate an automatic algorithm for calculating the gait features of a trajectory extracted from video recorded using a single camera attached to the roof of a building. Our findings indicate that females have 28.64% smaller sway amplitudes, 8.68% smaller stride lengths, and 8.14% slower speeds compared to males, with no significant difference in frequency. However, according to further investigation, our study reveals that the body parameters are the main variables that dominate gait features rather than gender. We have conducted three experiments in which the volunteers are walking towards the destination a) without any obstruction, b) with a stationary non-living obstacle present in the middle of the path, and c) with a human being standing in the middle of the path. From a comprehensive statistical analysis, key observations include no significant difference in gait features with respect to gender, no significant difference in gait features in the absence or presence of an obstacle, pedestrians treating stationary human beings and stationary obstacles the same given that the gender is same to match the comfort level, and a directional bias towards the left direction, likely influenced by left-hand traffic rule in India.