This paper presents a comprehensive study on scene analysis inside a train using computer vision and deep learning techniques. The primary objective is to develop a dynamic 2D representation of the perceived scene by continuously projecting passengers onto a 2D plan. The research contributions focus on detecting abnormal situations inside a train, such as passenger discomfort and forgotten luggage. To achieve optimal results, we rigorously tested cutting-edge deep learning techniques, including convolutional neural networks (CNN) for object detection and tracking. Through fine-tuning pretrained models on annotated datasets, our system achieves precise localization of individuals, enabling accurate estimation of their actions. Additionally, temporal analysis is used to moderate anomaly detection mechanisms for identifying situations requiring attention. The AI-based system has undergone rigorous real-time evaluation using RGB cameras installed in an actual train car. Through an onboard train sever, our system proactively identifies potential abnormal situations and sends alert to a supervision application. During testing sessions on the train, several scenarios involving unattended luggage and passenger discomfort were successfully identified in real-time, with high confidence rates.