Predicting the Driver's Focus of Attention: the DR(eye)VE Project Andrea Palazzi, Davide Abati, Simone Calderara, Francesco Solera, Rita Cucchiara (Submitted on 10 May 2017 (v1), last revised 6 Jun 2018 (this version, v3)) In this work we aim to predict the driver's focus of attention. The goal is to estimate what a person would pay attention to while driving, and which part of the scene around the vehicle is more critical for the task. To this end we propose a new computer vision model based on a multi-branch deep architecture that integrates three sources of information: raw video, motion and scene semantics. We also introduce DR(eye)VE, the largest dataset of driving scenes for which eye-tracking annotations are available. This dataset features more than 500,000 registered frames, matching ego-centric views (from glasses worn by drivers) and car-centric views (from roof-mounted camera), further enriched by other sensors measurements. Results highlight that several attention patterns are shared across drivers and can be reproduced to some extent. The indication of which elements in the scene are likely to capture the driver's attention may benefit several applications in the context of human-vehicle interaction and driver attention analysis.

Predicting the Driver's Focus of Attention: the DR(eye)VE Project / Palazzi, Andrea; Abati, Davide; Calderara, Simone; Solera, Francesco; Cucchiara, Rita. - In: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE. - ISSN 0162-8828. - 41:7(2019), pp. 1720-1733. [10.1109/TPAMI.2018.2845370]

Predicting the Driver's Focus of Attention: the DR(eye)VE Project

Palazzi, Andrea;Abati, Davide;Calderara, Simone;Solera, Francesco;Cucchiara, Rita
2019

Abstract

Predicting the Driver's Focus of Attention: the DR(eye)VE Project Andrea Palazzi, Davide Abati, Simone Calderara, Francesco Solera, Rita Cucchiara (Submitted on 10 May 2017 (v1), last revised 6 Jun 2018 (this version, v3)) In this work we aim to predict the driver's focus of attention. The goal is to estimate what a person would pay attention to while driving, and which part of the scene around the vehicle is more critical for the task. To this end we propose a new computer vision model based on a multi-branch deep architecture that integrates three sources of information: raw video, motion and scene semantics. We also introduce DR(eye)VE, the largest dataset of driving scenes for which eye-tracking annotations are available. This dataset features more than 500,000 registered frames, matching ego-centric views (from glasses worn by drivers) and car-centric views (from roof-mounted camera), further enriched by other sensors measurements. Results highlight that several attention patterns are shared across drivers and can be reproduced to some extent. The indication of which elements in the scene are likely to capture the driver's attention may benefit several applications in the context of human-vehicle interaction and driver attention analysis.
8-giu-2018
41
7
1720
1733
Predicting the Driver's Focus of Attention: the DR(eye)VE Project / Palazzi, Andrea; Abati, Davide; Calderara, Simone; Solera, Francesco; Cucchiara, Rita. - In: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE. - ISSN 0162-8828. - 41:7(2019), pp. 1720-1733. [10.1109/TPAMI.2018.2845370]
Palazzi, Andrea; Abati, Davide; Calderara, Simone; Solera, Francesco; Cucchiara, Rita
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1162421
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