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

Vehicle detection in real-time scenarios is challenging because of the timeconstraints and the presence of multiple types of vehicles with differentspeeds, shapes, structures, etc. This paper presents a new method relied ongenerating a confidence map-for robust and faster vehicle detection. To reducethe adverse effect of different speeds, shapes, structures, and the presence ofseveral vehicles in a single image, we introduce the concept of augmentationwhich highlights the region of interest containing the vehicles. The augmentedmap is generated by exploring the combination of multiresolution analysis andmaximally stable extremal regions (MR-MSER). The output of MR-MSER is suppliedto fast CNN to generate a confidence map, which results in candidate regions.Furthermore, unlike existing models that implement complicated models forvehicle detection, we explore the combination of a rough set and fuzzy-basedmodels for robust vehicle detection. To show the effectiveness of the proposedmethod, we conduct experiments on our dataset captured by drones and on severalvehicle detection benchmark datasets, namely, KITTI and UA-DETRAC. The resultson our dataset and the benchmark datasets show that the proposed methodoutperforms the existing methods in terms of time efficiency and achieves agood detection rate.

Robust and Fast Vehicle Detection using Augmented Confidence Map