Pedestrian gait analysis using automated computer vision techniques

In sustainable urban planning, non-motorised active modes of travel such as walking are identified as a leading driver for a healthy, liveable and resource-efficient environment. To encourage walking, there is a need for a solid understanding of pedestrian walking behaviour. This understanding is central to the evaluation of measures of walking conditions such as comfortability and efficiency. The main purpose of this study is to gain an in-depth understanding of pedestrian walking behaviour through the investigation of the spatio-temporal gait parameters (step length and step frequency). This microscopic-level analysis provides insight into the pedestrian walking mechanisms and the effect of various attributes such as gender and age. This analysis relies on automated video-based data collection using computer vision techniques. The step frequency and step length are estimated based on oscillatory patterns in the walking speed profile. The study uses real-world video data collected in downtown Vancouver, BC. The results show that the gait parameters are influenced by factors such as crosswalk grade, pedestrian gender, age and group size. The step length was found to generally have more influence on walking speed than step frequency. It was also found that, compared to males, females increase their step frequency to increase their walking speed.