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Forecasting pedestrian trajectory with machine-annotated training data
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Styles, Olly, Ross, Arun and Sanchez Silva, Victor (2019) Forecasting pedestrian trajectory with machine-annotated training data. In: 2019 IEEE Intelligent Vehicles Symposium (IV’19), Paris, France, 9-12 Jun 2019. Published in: 2019 IEEE Intelligent Vehicles Symposium (IV) doi:10.1109/IVS.2019.8814207 ISSN 2642-7214 .
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Official URL: http://dx.doi.org/10.1109/IVS.2019.8814207
Abstract
Reliable anticipation of pedestrian trajectory is imperative for the operation of autonomous vehicles and can significantly enhance the functionality of advanced driver assistance systems. While significant progress has been made in the field of pedestrian detection, forecasting pedestrian trajectories remains a challenging problem due to the unpredictable nature of pedestrians and the huge space of potentially useful features. In this work, we present a deep learning approach for pedestrian trajectory forecasting using a single vehicle mounted camera.Deep learning models that have revolutionized other areas in computer vision have seen limited application to trajectory forecasting, in part due to the lack of richly annotated training data. We address the lack of training data by introducing a scalable machine annotation scheme that enables our model to be trained using a large dataset without human annotation.In addition,we propose Dynamic Trajectory Predictor (DTP), a model for forecasting pedestrian trajectory up to one second into the future. DTP is trained using both human and machine-annotated data, and anticipates dynamic motion that is not captured by linear models. Experimental evaluation confirms the benefits of the proposed model.
Item Type: | Conference Item (Poster) | |||||||||
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Subjects: | T Technology > TL Motor vehicles. Aeronautics. Astronautics | |||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
Library of Congress Subject Headings (LCSH): | Autonomous vehicles, Pedestrians | |||||||||
Journal or Publication Title: | 2019 IEEE Intelligent Vehicles Symposium (IV) | |||||||||
Publisher: | IEEE | |||||||||
ISSN: | 2642-7214 | |||||||||
Official Date: | 29 August 2019 | |||||||||
Dates: |
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DOI: | 10.1109/IVS.2019.8814207 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Reuse Statement (publisher, data, author rights): | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||
Date of first compliant deposit: | 14 May 2019 | |||||||||
Date of first compliant Open Access: | 15 May 2019 | |||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Poster | |||||||||
Title of Event: | 2019 IEEE Intelligent Vehicles Symposium (IV’19) | |||||||||
Type of Event: | Conference | |||||||||
Location of Event: | Paris, France | |||||||||
Date(s) of Event: | 9-12 Jun 2019 | |||||||||
Related URLs: |
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