In the field of train transportation, personal injuries due to train automatic doors are still a common occurrence. This paper aims at implementing a computer vision solution as part of a safety detection system to identify automatic doors-related hazardous events to reduce their occurrence and their severity. Deep anomaly detection algorithms are often applied on CCTV video feeds to identify such hazardous events. However, the anomalous events identified by those algorithms are often simpler than most common occurrences in transport environments, hindering their widespread usage. Since such events are of quite a diverse nature and no dataset featuring them exist, we create a specilically-tailored dataset composed of real-case scenarios of hazardous events near train doors. We then study an anomaly detection algorithm from the literature on this dataset and propose a set of modifications to better adapt it to our railway context and to subsequently ease its application to a wider range of use-cases.