English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The intelligent vehicle community has devoted considerable efforts to modeldriver behavior, and in particular to detect and overcome driver distraction inan effort to reduce accidents caused by driver negligence. However, as thedomain increasingly shifts towards autonomous and semi-autonomous solutions,the driver is no longer integral to the decision making process, indicating aneed to refocus efforts elsewhere. To this end, we propose to study pedestriandistraction instead. In particular, we focus on detecting pedestrians who areengaged in secondary activities involving their cellphones and similar handheldmultimedia devices from a purely vision-based standpoint. To achieve thisobjective, we propose a pipeline incorporating articulated human poseestimation, followed by a soft object label transfer from an ensemble ofexemplar SVMs trained on the nearest neighbors in pose feature space. Weadditionally incorporate head gaze features and prior pose information to carryout cellphone related pedestrian activity recognition. Finally, we offer amethod to reliably track the articulated pose of a pedestrian through asequence of images using a particle filter with a Gaussian Process DynamicalModel (GPDM), which can then be used to estimate sequentially varying activityscores at a very low computational cost. The entire framework is fast(especially for sequential data) and accurate, and easily extensible to includeother secondary activities and sources of distraction.

When Vehicles See Pedestrians with Phones:A Multi-Cue Framework for Recognizing Phone-based Activities of Pedestrians