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\section{Conclusion}  This paper demonstrates the theoretical and practical application of a large-scale, automated, video data collection system using computer vision for highly detailed traffic studies, particularly proactive road safety analysis using surrogate safety  analysis. The reader is led step-by-step through the process of collecting, processing, and analysing video data with data, presenting  examples and discussing  challenges along the way. It demonstrates an early implementation in the form of a cross-sectional analysis of driver behaviour in a large set of roundabout video data testing for contributing factors. .  Several technical challenges and their solutions were outlined, notably tracking error (accuracy optimised up to $94\%$ and no less than $84\%$ $85\%$  using the comprehensive MOTA methodology), quantified probability analysis  of collision from TTCs, TTC distributions,  and aggregation and sampling considerations, as they still require particular attention. considerations.  It is expected that these issues will be further addressed as processing and analysis tools become more accessible, more collaborators contribute solutions to the open-source software stack, and as techniques applied to transportation issues become more sophisticated. The full results of the study over all 600 hours of video data Future work  will be the subject examine camera lens, angle  and focus of future papers. More advanced tracking, error detection, motion prediction models, visibility considerations for tracking accuracy, before  and trajectory clustering will also be after studies, and comprehensive analysis of  the subject contributing factors  of further research. the roundabout data set.