Development of an Evaluation System for Outdoor Advertising Using Smartphone Eye Tracking

Due to the population aging in Japan, more elderly people are retaining their driver’s licenses and the increase in the number of car accidents by elderly drivers is a social problem. To address this problem, an objective data-based method to evaluate whether elderly drivers can continue driving is needed. In this paper, we propose a car driving evaluation system based on gaze as calculated by eye and head angles. We used an eye tracking device (TalkEye Lite) made by the Takei Scientific Instruments Cooperation. For our image processing technique, we propose a gaze fixation condition using deep learning (YOLOv2-tiny). By using an eye tracking device and the proposed gaze fixation condition, we built a system where drivers could be evaluated during actual car operation. We describe our system in this paper. In order to evaluate our proposed method, we conducted experiments from November 2017 to November 2018 where elderly people were evaluated by our system while driving an actual car. The subjects were 22 general drivers (two were 80–89 years old, four were 70–79 years old, six were 60–69 years old, three were 50–59 years old, five were 40–49 years old and two were 30–39 years old). We compared the subjects’ gaze information with the subjective evaluation by a professional driving instructor. As a result, we confirm that the subjects’ gaze information is related to the subjective evaluation by the instructor.

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Using Eye-tracking and Deep Learning Approach to Promote Naturalness in Urban Landscape

10.5194/egusphere-egu2020-6515 ◽

2020 ◽

Author(s):

Yeshan Qiu ◽

Yugang Chen ◽

Shengquan Che

Keyword(s):

Deep Learning ◽

Eye Tracking ◽

Bayesian Network ◽

Evaluation System ◽

Public Perception ◽

Urban Landscape ◽

Evaluation Model ◽

Landscape Composition ◽

Evaluation Framework ◽

Aesthetic Evaluation

<p>Promoting greenness and naturalness has been the integral goal in nature-based solutions for urban environments. Design and building appreciated landscape for subjective public perception is a key factor in the success of promoting urban greenness and naturalness. The current measures of naturalness are siloed from public appreciation and acceptance of urban landscape designs. Our goal is to use state-of-art methods combining traditional design perception evaluation to embed naturalness with public landscape aesthetic perceptions evaluation system. A deep learning and eye-tracking based approach to understand public aesthetic perceptions of landscape street-view images is developed and applied to a case study of Shanghai. We use machine deep learning techniques to identify and assess landscape composition with landscape images and in-situ captured data to study the influence of naturalness of public perceptions of landscape based on a Bayesian network aesthetic evaluation model. The methodology extend the present landscape aesthetic evaluation framework and has the potential to be implemented to much wider applications. Our results indicate a co-conception of naturalness and public appreciation as a proof-of-concept of nature-based solutions.</p><p>Key words&#65306;Eye-tracking&#65307;Deep Learning&#65307;Naturalness&#65307;Public aesthetic perceptions&#65307;Bayesian network aesthetic evaluation</p>

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An Eye-Tracking Study on Audiovisual Speech Perception Strategies Adopted by Normal-Hearing and Deaf Adults Under Different Language Familiarities

Journal of Speech Language and Hearing Research ◽

10.1044/2020_jslhr-19-00223 ◽

2020 ◽

Vol 63 (7) ◽

pp. 2245-2254 ◽

Cited By ~ 1

Author(s):

Jianrong Wang ◽

Yumeng Zhu ◽

Yu Chen ◽

Abdilbar Mamat ◽

Mei Yu ◽

...

Keyword(s):

Speech Perception ◽

Eye Tracking ◽

Normal Hearing ◽

Audiovisual Speech ◽

Audiovisual Speech Perception ◽

Allocation Pattern ◽

Deaf Adults ◽

Bilingual Speaker ◽

Primary Hypothesis ◽

Standard Chinese

Purpose The primary purpose of this study was to explore the audiovisual speech perception strategies.80.23.47 adopted by normal-hearing and deaf people in processing familiar and unfamiliar languages. Our primary hypothesis was that they would adopt different perception strategies due to different sensory experiences at an early age, limitations of the physical device, and the developmental gap of language, and others. Method Thirty normal-hearing adults and 33 prelingually deaf adults participated in the study. They were asked to perform judgment and listening tasks while watching videos of a Uygur–Mandarin bilingual speaker in a familiar language (Standard Chinese) or an unfamiliar language (Modern Uygur) while their eye movements were recorded by eye-tracking technology. Results Task had a slight influence on the distribution of selective attention, whereas subject and language had significant influences. To be specific, the normal-hearing and the d10eaf participants mainly gazed at the speaker's eyes and mouth, respectively, in the experiment; moreover, while the normal-hearing participants had to stare longer at the speaker's mouth when they confronted with the unfamiliar language Modern Uygur, the deaf participant did not change their attention allocation pattern when perceiving the two languages. Conclusions Normal-hearing and deaf adults adopt different audiovisual speech perception strategies: Normal-hearing adults mainly look at the eyes, and deaf adults mainly look at the mouth. Additionally, language and task can also modulate the speech perception strategy.

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Activation and Persistence of Implicit Causality Information in Spoken Language Comprehension

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000002 ◽

2010 ◽

Vol 57 (1) ◽

pp. 5-16 ◽

Cited By ~ 46

Author(s):

Pirita Pyykkönen ◽

Juhani Järvikivi

Keyword(s):

Eye Tracking ◽

Language Comprehension ◽

Spoken Language ◽

Visual World ◽

Implicit Causality ◽

Causality Information ◽

Spoken Language Comprehension

A visual world eye-tracking study investigated the activation and persistence of implicit causality information in spoken language comprehension. We showed that people infer the implicit causality of verbs as soon as they encounter such verbs in discourse, as is predicted by proponents of the immediate focusing account ( Greene & McKoon, 1995 ; Koornneef & Van Berkum, 2006 ; Van Berkum, Koornneef, Otten, & Nieuwland, 2007 ). Interestingly, we observed activation of implicit causality information even before people encountered the causal conjunction. However, while implicit causality information was persistent as the discourse unfolded, it did not have a privileged role as a focusing cue immediately at the ambiguous pronoun when people were resolving its antecedent. Instead, our study indicated that implicit causality does not affect all referents to the same extent, rather it interacts with other cues in the discourse, especially when one of the referents is already prominently in focus.

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