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\section{Conclusion}  Large-scale automated video data allows for larger surrogate analysis, in practice and research, for the same purpose as the traditional historical accident data approach: road network screening, evaluation of countermeasure, road safety diagnosis, etc. With this in mind, surrogate safety analysis is expected to play an important role as a complementary safety approach or as an approach that can potentially replace the traditional methods, particularly when historical accident data is limited or doubtful (given its poor quality in some cases).  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, in particular, particular for  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 examples and discussion of challenges along the way. This paper demonstrates an early implementation of the methodology in the form of a cross-sectional analysis of driver behaviour in the largest set of roundabout video data analysed to date. Several technical challenges and their solutions were outlined, notably tracking errors (MOTA optimized to $94\%$ and no less than $85\%$), analysis of TTC distributions, and aggregation and sampling 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 techniques applied to transportation issues become more sophisticated. Future work will examine camera lens, angle, and visibility considerations for effect on tracking accuracy.