About: Does agreement mean accuracy? Evaluating glance annotation in naturalistic driving data

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Does agreement mean accuracy? Evaluating glance annotation in naturalistic driving data
Creator	Hermens, Frouke Jansen, Reinier Van Der Kint, Sander
Source	Medline; PMC
abstract	Naturalistic driving studies often make use of cameras to monitor driver behavior. To analyze the resulting video images, human annotation is often adopted. These annotations then serve as the ‘gold standard’ to train and evaluate automated computer vision algorithms, even though it is uncertain how accurate human annotation is. In this study, we provide a first evaluation of glance direction annotation by comparing instructed, actual glance direction of truck drivers with annotated direction. Findings indicate that while for some locations high annotation accuracy is achieved, for most locations accuracy is well below 50%. Higher accuracy can be obtained by clustering these locations, but this also leads to reduced detail of the annotation, suggesting that decisions to use clustering should take into account the purpose of the annotation. The data also show that high agreement between annotators does not guarantee high accuracy. We argue that the accuracy of annotation needs to be verified experimentally more often.
has issue date	2020-07-29(xsd:dateTime)
bibo:doi	10.3758/s13428-020-01446-9
bibo:pmid	32728917
has license	no-cc
sha1sum (hex)	d83b72a7c8534b075dd587b6962989f860eae359
schema:url	https://doi.org/10.3758/s13428-020-01446-9
resource representing a document's title	Does agreement mean accuracy? Evaluating glance annotation in naturalistic driving data
has PubMed Central identifier	PMC7390446
has PubMed identifier	32728917
schema:publication	Behav Res Methods
resource representing a document's body	covid:d83b72a7c8534b075dd587b6962989f860eae359#body_text
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