Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior

By Amir Rasouli, Iuliia Kotseruba, John K. Tsotsos. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017

402 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/04964e2697778dc843671c7764f0f912e46991ca

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. John K. Tsotsos
2. Amir Rasouli
3. Kate Saenko
4. Iuliia Kotseruba

In Computer Vision and Pattern Recognition

1. John K. Tsotsos
2. Amir Rasouli
3. Iuliia Kotseruba
4. Kate Saenko
5. Luc Van Gool
6. Yi-Ting Chen
7. Vasili Ramanishka
8. Philip H. S. Torr

Citations in the corpus

• Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice (2018, methodology): “of 80% [142], 62% [14] for the probability of crossing, and”
• Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning (2018, background): “, [26] provides a dataset (Joint Attention in Autonomous Driving) with annotations for studying pedestrian crosswalk behaviors.”
• PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction (2019, background): “A recently proposed dataset, JAAD [27], contains a large number of pedestrian samples with temporal correspondence, a subset of which are annotated with behavior information.”