Scipediacontent: Scipediacontent moved page Draft Content 435258555 to Englund et al 2018a

2021-01-28T17:19:13Z

Scipediacontent moved page Draft Content 435258555 to Englund et al 2018a

Scipediacontent: Created page with " == Abstract == on and intention recognition of pedestrians in urban settings are challenging problems for Advanced Driver Assistance Systems as well as future autonomous veh..."

2021-01-28T17:19:09Z

Created page with " == Abstract == on and intention recognition of pedestrians in urban settings are challenging problems for Advanced Driver Assistance Systems as well as future autonomous veh..."

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== Abstract ==

on and intention recognition of pedestrians in urban settings are challenging problems for Advanced Driver Assistance Systems as well as future autonomous vehicles to maintain smooth and safe traffic. This work investigates a number of feature extraction methods in combination with several machine learning algorithms to build knowledge on how to automatically detect the action and intention of pedestrians in urban traffic. We focus on the motion and head orientation to predict whether the pedestrian is about to cross the street or not. The work is based on the Joint Attention for Autonomous Driving (JAAD) dataset, which contains 346 videoclips of various traffic scenarios captured with cameras mounted in the windshield of a car. An accuracy of 72% for head orientation estimation and 85% for motion detection is obtained in our experiments.

Comment: Accepted for publication at Intl Conf on Signal Image Technology & Internet Based Systems, SITIS 2018

== Original document ==

The different versions of the original document can be found in:

* [http://arxiv.org/abs/1810.09805 http://arxiv.org/abs/1810.09805]

* [http://arxiv.org/pdf/1810.09805 http://arxiv.org/pdf/1810.09805]

* [http://xplorestaging.ieee.org/ielx7/8693683/8705930/08706225.pdf?arnumber=8706225 http://xplorestaging.ieee.org/ielx7/8693683/8705930/08706225.pdf?arnumber=8706225],
: [http://dx.doi.org/10.1109/sitis.2018.00109 http://dx.doi.org/10.1109/sitis.2018.00109]

* [https://dblp.uni-trier.de/db/journals/corr/corr1810.html#abs-1810-09805 https://dblp.uni-trier.de/db/journals/corr/corr1810.html#abs-1810-09805],
: [http://www.diva-portal.org/smash/record.jsf?pid=diva2:1268689 http://www.diva-portal.org/smash/record.jsf?pid=diva2:1268689],
: [http://ui.adsabs.harvard.edu/abs/2018arXiv181009805V/abstract http://ui.adsabs.harvard.edu/abs/2018arXiv181009805V/abstract],
: [http://ri.diva-portal.org/smash/record.jsf?pid=diva2:1283344 http://ri.diva-portal.org/smash/record.jsf?pid=diva2:1283344],
: [http://hh.diva-portal.org/smash/record.jsf?pid=diva2:1268689 http://hh.diva-portal.org/smash/record.jsf?pid=diva2:1268689],
: [https://academic.microsoft.com/#/detail/2896127393 https://academic.microsoft.com/#/detail/2896127393]

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Englund et al 2018a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 435258555 to Englund et al 2018a

Scipediacontent: Created page with " == Abstract == on and intention recognition of pedestrians in urban settings are challenging problems for Advanced Driver Assistance Systems as well as future autonomous veh..."