Proceedings ArticleDOI
Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior
Amir Rasouli,Iuliia Kotseruba,John K. Tsotsos +2 more
- pp 206-213
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TLDR
A novel dataset is introduced which in addition to providing the bounding box information for pedestrian detection, also includes the behavioral and contextual annotations for the scenes, which allows combining visual and semantic information for better understanding of pedestrians' intentions in various traffic scenarios.Abstract:
Designing autonomous vehicles suitable for urban environments remains an unresolved problem. One of the major dilemmas faced by autonomous cars is how to understand the intention of other road users and communicate with them. The existing datasets do not provide the necessary means for such higher level analysis of traffic scenes. With this in mind, we introduce a novel dataset which in addition to providing the bounding box information for pedestrian detection, also includes the behavioral and contextual annotations for the scenes. This allows combining visual and semantic information for better understanding of pedestrians' intentions in various traffic scenarios. We establish baseline approaches for analyzing the data and show that combining visual and contextual information can improve prediction of pedestrian intention at the point of crossing by at least 20%.read more
Citations
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Journal ArticleDOI
CAPformer: Pedestrian Crossing Action Prediction Using Transformer.
Javier Lorenzo,Ignacio Parra Alonso,R. Izquierdo,Augusto Luis Ballardini,Álvaro Hernández Saz,David Fernández Llorca,Miguel Angel Sotelo +6 more
TL;DR: In this article, the authors proposed a self-attention alternative based on transformer architecture, which has had enormous success in natural language processing (NLP) and recently in computer vision.
Journal ArticleDOI
Detection of Pedestrian Actions Based on Deep Learning Approach
TL;DR: A pedestrian detection component based on Faster R-CNN able to detect the pedestrian and also recognize if the pedestrian is crossing the street in the detecting time is proposed.
Proceedings ArticleDOI
Crossing-Road Pedestrian Trajectory Prediction Based on Intention and Behavior Identification
TL;DR: Zhang et al. as mentioned in this paper presented a pedestrian trajectory prediction method that involves pedestrian intention and behavior information into prediction, which shows good scenario adaptability and provides accurate path prediction results for eight defined typical pedestrian crossing-road scenarios in the prediction horizon, especially for stopping scenarios.
Posted Content
Bifold and Semantic Reasoning for Pedestrian Behavior Prediction
TL;DR: In this paper, a multi-task learning framework was proposed to predict trajectories and actions of pedestrians by relying on multimodal data, where different data modalities are processed independently allowing them to develop their own representations, and jointly to produce a representation for all modalities using shared parameters.
Posted Content
Pedestrian Behavior Prediction for Automated Driving: Requirements, Metrics, and Relevant Features.
Michael Herman,Jörg Wagner,Vishnu Prabhakaran,Nicolas Möser,Hanna Ziesche,Waleed Ahmed,Lutz Bürkle,Ernst Kloppenburg,Claudius Gläser +8 more
TL;DR: A pedestrian prediction model based on a Conditional Variational Auto-Encoder (CVAE) which incorporates multiple contextual cues to achieve accurate long-term prediction is presented which shows superior performance over a baseline prediction model.
References
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