Exploring Feature Distributions from Pedestrian Trajectories

Motivation In this article, I explore the statistical distributions of features extracted from pedestrian trajectories. Although the tracking accuracy still has room for improvement, analyzing the distributions of speed and related features allows me to characterize average pedestrian behavior from real video data. I have recently been studying information geometry, and I want to investigate how pedestrian behavior changes across locations by modeling it as probability distributions. By constructing a statistical manifold from these distributions across multiple locations, I aim to capture differences between pedestrian populations that conventional clustering methods such as k-means — which rely on Euclidean distance — cannot detect. Note I use AI assistance to draft and polish the English, but the analysis, interpretation, and core ideas are my own. Learning to write technical English is itself part of this project. Introduction In Article 1, I detected pedestrians in video footage a