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

We propose a deep neural network fusion architecture for fast and robustpedestrian detection. The proposed network fusion architecture allows forparallel processing of multiple networks for speed. A single shot deepconvolutional network is trained as a object detector to generate all possiblepedestrian candidates of different sizes and occlusions. This network outputs alarge variety of pedestrian candidates to cover the majority of ground-truthpedestrians while also introducing a large number of false positives. Next,multiple deep neural networks are used in parallel for further refinement ofthese pedestrian candidates. We introduce a soft-rejection based network fusionmethod to fuse the soft metrics from all networks together to generate thefinal confidence scores. Our method performs better than existingstate-of-the-arts, especially when detecting small-size and occludedpedestrians. Furthermore, we propose a method for integrating pixel-wisesemantic segmentation network into the network fusion architecture as areinforcement to the pedestrian detector. The approach outperformsstate-of-the-art methods on most protocols on Caltech Pedestrian dataset, withsignificant boosts on several protocols. It is also faster than all othermethods.

Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection