Will the Pedestrian Cross? A Study on Pedestrian Path Prediction
By Christoph Gustav Keller, Dariu M. Gavrila. IEEE transactions on intelligent transportation systems (Print), 2014
313 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Automotive Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/3ca983d40b9de7dc12b989fce213b4abee652c9e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Kris M. Kitani
- Fabian B. Flohr
- Michael Herman
- Kai Oliver Arras
- Luigi Palmieri
- Andrey Rudenko
- Nicolas Schneider
- Julian F. P. Kooij
In Automotive Engineering
- Fabian B. Flohr
- Kai Oliver Arras
- Julian F. P. Kooij
- Nicolas Schneider
- Kris M. Kitani
- Andrey Rudenko
- Luigi Palmieri
- Michael Herman
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Kris M. Kitani
- Kristen Grauman
- Bernt Schiele
- Jitendra Malik
- Andrew Zisserman
- Trevor Darrell
- Xiaogang Wang
Citations in the corpus
- Human motion trajectory prediction: a survey (2019, methodology): “In their PHTM approach Keller and Gavrila (2014) propose a probabilistic search tree of sample human trajectory snippets to find the corresponding matching sub-sequence.”
- Context-Based Pedestrian Path Prediction (2014, methodology): “Both approaches were shown to perform similar, and outperform the first-order Markov LDS and SLDS models, albeit at a large computational cost ([18] reports GPDM/PHTM is three/two orders of magnitude slower than KF).”
- Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning (2018, background): “Robust pedestrian behavior modeling is also necessary [17, 16].”