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

In Intelligent Transportation System, real-time systems that monitor andanalyze road users become increasingly critical as we march toward the smartcity era. Vision-based frameworks for Object Detection, Multiple ObjectTracking, and Traffic Near Accident Detection are important applications ofIntelligent Transportation System, particularly in video surveillance and etc.Although deep neural networks have recently achieved great success in manycomputer vision tasks, a uniformed framework for all the three tasks is stillchallenging where the challenges multiply from demand for real-timeperformance, complex urban setting, highly dynamic traffic event, and manytraffic movements. In this paper, we propose a two-stream Convolutional Networkarchitecture that performs real-time detection, tracking, and near accidentdetection of road users in traffic video data. The two-stream model consists ofa spatial stream network for Object Detection and a temporal stream network toleverage motion features for Multiple Object Tracking. We detect near accidentsby incorporating appearance features and motion features from two-streamnetworks. Using aerial videos, we propose a Traffic Near Accident Dataset(TNAD) covering various types of traffic interactions that is suitable forvision-based traffic analysis tasks. Our experiments demonstrate the advantageof our framework with an overall competitive qualitative and quantitativeperformance at high frame rates on the TNAD dataset.

Intelligent Intersection: Two-Stream Convolutional Networks for Real-time Near Accident Detection in Traffic Video