# Closed-Circuit Television Data as an Emergent Data Source for Urban Rail Platform Crowding Estimation

arXiv: https://arxiv.org/abs/2508.03749 · HTML: https://mlsanity.com/paper/2508.03749/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 3 Aug 2025.

## Builds on (most cited first, 12 of 12)

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- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [CrowdHuman: A Benchmark for Detecting Human in a Crowd](https://mlsanity.com/paper/1805.00123/) (2018)
- [RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer](https://mlsanity.com/paper/2407.17140/) (2024)
- [Rethinking Spatial Invariance of Convolutional Networks for Object Counting](https://mlsanity.com/paper/2206.05253/) (2022)
- [Improving Point-based Crowd Counting and Localization Based on Auxiliary Point Guidance](https://mlsanity.com/paper/2405.10589/) (2024)
- [Computer Vision for Transit Travel Time Prediction: An End-to-End Framework Using Roadside Urban Imagery](https://mlsanity.com/paper/2211.12322/) (2022)

## Abstract (arXiv)

Accurately estimating urban rail platform occupancy can support more informed operational decision-making by transit agencies, particularly during periods of crowding. However, sensing real-time platform occupancy remains challenging and often relies 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 for accurate, real-time occupancy estimation. The present study investigates this approach 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 and efficient convex ridge regression over mean-pooled segmentation features to extract counts from the generated segmentation maps while accounting for image depth and passenger dispersion along a platform. Tested on a privacy-preserving dataset, created in collaboration with the Washington Metropolitan Area Transit Authority (WMATA), and including 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 transit agencies, can enable real-time crowding estimation and, eventually, timely operational responses to mitigate platform crowding.