A NTUA Diploma Thesis titled “Investigation of Non-Compliant Pedestrian Crossings at Signalized Intersections Using Computer Vision Techniques” was recently presented by Mirogianni Melpomeni. For this reason an advanced video-based detection algorithm using data collected from a high-traffic intersection in Omonoia Square, in Athens was utilized. The dataset included detailed pedestrian and vehicle coordinates and speed characteristics, signal timing, and time-to-collision metric. The analysis consisted logistic regression, random forest classification, and point-biserial correlation to identify significant predictors of non-compliant behaviour and also to compare the effectiveness of the manual field and computer vision algorithm results. The findings contribute to the understanding of pedestrian violations and offer valuable insights for future implementation of automated monitoring systems and policy interventions for safer crosswalks.