BriefCam tackles the formidable challenges of video surveillance through video content analytics (VCA), utilizing advanced AI to evaluate video streams, either recorded or in real time, turning the continual stream of visuals and movement into a searchable, quantifiable and actionable data outcome for transportation hubs. BriefCam utilizes deep learning-the combination of multiple machine learning algorithms-to drive these actionable insights. By creating a video-to-insight pipeline, BriefCam creates a structured database from unstructured video data, enabling that resulting information to be searchable, quantifiable, and most importantly, actionable. This processing can be done either in real time, or at scheduled intervals depending on the video information and the data needs or actionability. Through object extraction, BriefCam can remove backgrounds and other extraneous data, reducing the "noise" from videos, speeding the detection and tracking efforts. Through its two-tier categorization, BriefCam reports over 96% accuracy for first level identification with an additional 86% average accuracy claimed by BriefCam for the more detailed subclasses that follow.