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 vast number of existing IP cameras in current road networks is anopportunity to take advantage of the captured data and analyze the video anddetect any significant events. For this purpose, it is necessary to detectmoving vehicles, a task that was carried out using classical artificial visiontechniques until a few years ago. Nowadays, significant improvements have beenobtained by deep learning networks. Still, object detection is considered oneof the leading open issues within computer vision.  The current scenario is constantly evolving, and new models and techniquesare appearing trying to improve this field. In particular, new problems anddrawbacks appear regarding detecting small objects, which correspond mainly tothe vehicles that appear in the road scenes. All this means that new solutionsthat try to improve the low detection rate of small elements are essential.Among the different emerging research lines, this work focuses on the detectionof small objects. In particular, our proposal aims to vehicle detection fromimages captured by video surveillance cameras.  In this work, we propose a new procedure for detecting small-scale objects byapplying super-resolution processes based on detections performed byconvolutional neural networks \emph{(CNN)}. The neural network is integratedwith processes that are in charge of increasing the resolution of the images toimprove the object detection performance. This solution has been tested for aset of traffic images containing elements of different scales to test theefficiency according to the detections obtained by the model, thusdemonstrating that our proposal achieves good results in a wide range ofsituations.

Improved detection of small objects in road network sequences