# From Pedestrian Detection to Crosswalk Estimation: An EM Algorithm and Analysis on Diverse Datasets

arXiv: https://arxiv.org/abs/2205.12579 · HTML: https://mlsanity.com/paper/2205.12579/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 33 references that are in the map.
- First posted on arXiv 25 May 2022.

## Builds on (most cited first, 2 of 2)

- [Pedestrian Path, Pose and Intention Prediction through Gaussian Process Dynamical Models and Pedestrian Activity Recognition](https://mlsanity.com/paper/2004.14747/) (2020)
- [Automatic Large-Scale Data Acquisition via Crowdsourcing for Crosswalk Classification: A Deep Learning Approach](https://mlsanity.com/paper/1805.11970/) (2018)

## Built on by (most cited first, 3 of 3)

- [Safe Control Transitions: Machine Vision Based Observable Readiness Index and Data-Driven Takeover Time Prediction](https://mlsanity.com/paper/2301.05805/) (2023)
- [Pedestrian Behavior Maps for Safety Advisories: CHAMP Framework and Real-World Data Analysis](https://mlsanity.com/paper/2305.04506/) (2023)
- [CHAMP: Crowdsourced, History-Based Advisory of Mapped Pedestrians for Safer Driver Assistance Systems](https://mlsanity.com/paper/2301.05842/) (2023)

## Abstract (arXiv)

In this work, we contribute an EM algorithm for estimation of corner points and linear crossing segments for both marked and unmarked pedestrian crosswalks using the detections of pedestrians from processed LiDAR point clouds or camera images. We demonstrate the algorithmic performance by analyzing three real-world datasets containing multiple periods of data collection for four-corner and two-corner intersections with marked and unmarked crosswalks. Additionally, we include a Python video tool to visualize the crossing parameter estimation, pedestrian trajectories, and phase intervals in our public source code.