Suicides in public places, such as railways, can have a significant impact on bystanders, railway staff, first responders and the surrounding communities. Behaviours prior to a suicide attempt have been identified, that could potentially be detected automatically. As a first step, the algorithm is required to accurately identify individuals exhibiting these behaviours in different settings. Our study analyses a human detection model focussing on pedestrian detection at railway stations as one component of a broader project to detect pre-suicidal behaviours. Closed-circuit television footage from two stations collected for the same 24-hour period were manually analysed to obtain parameters (true positives, false positives, and false negatives) which were then used to compute performance measures (sensitivity, precision, and F1 score). The model performed differently in both stations with a sensitivity of 0.73 and F1 score of 0.84 in Station A and a sensitivity of 0.48 and F1 score of 0.65 in Station B. Root causes of false negatives identified include differing body postures and occlusion. Although the model was adequate, its performance is dependent on the view captured by the cameras in stations. Collectively, these findings can be used to improve the model's performance.Clinical Relevance-Detecting behaviours prior to a suicide attempt offers a critical period for intervention by bystanders or first responders, potentially interrupting the attempt. This offers the potential to directly reduce suicide attempts, as well as reduce third-party exposure to these traumatic events.