Accurately estimating urban rail platform occupancy can enhance transit agencies' ability to make informed operational decisions, thereby improving safety, operational efficiency, and customer experience, particularly in the context of crowding. However, sensing real-time crowding remains challenging and often depends on indirect proxies such as automatic fare collection data or staff observations. Recently, Closed-Circuit Television (CCTV) footage has emerged as a promising data source with the potential to yield accurate, real-time occupancy estimates. The presented study investigates this potential by comparing three state-of-the-art computer vision approaches for extracting crowd-related features from platform CCTV imagery: (a) object detection and counting using YOLOv11, RT-DETRv2, and APGCC; (b) crowd-level classification via a custom-trained Vision Transformer, Crowd-ViT; and (c) semantic segmentation using DeepLabV3. Additionally, we present a novel, highly efficient linear-optimization-based approach to extract counts from the generated segmentation maps while accounting for image object depth and, thus, for passenger dispersion along a platform. Tested on a privacy-preserving dataset created in collaboration with the Washington Metropolitan Area Transit Authority (WMATA) that encompasses more than 600 hours of video material, our results demonstrate that computer vision approaches can provide substantive value for crowd estimation. This work demonstrates that CCTV image data, independent of other data sources available to a transit agency, can enable more precise real-time crowding estimation and, eventually, timely operational responses for platform crowding mitigation.