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/

Zendy Plus

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

Zendy Tools

Zendy Open

Studies of object detection and localization, particularly pedestriandetection have received considerable attention in recent times due to itsseveral prospective applications such as surveillance, driving assistance,autonomous cars, etc. Also, a significant trend of latest research studies inrelated problem areas is the use of sophisticated Deep Learning basedapproaches to improve the benchmark performance on various standard datasets. Atrade-off between the speed (number of video frames processed per second) anddetection accuracy has often been reported in the existing literature. In thisarticle, we present a new but simple deep learning based strategy forpedestrian detection that improves this trade-off. Since training of similarmodels using publicly available sample datasets failed to improve the detectionperformance to some significant extent, particularly for the instances ofpedestrians of smaller sizes, we have developed a new sample dataset consistingof more than 80K annotated pedestrian figures in videos recorded under varyingtraffic conditions. Performance of the proposed model on the test samples ofthe new dataset and two other existing datasets, namely Caltech PedestrianDataset (CPD) and CityPerson Dataset (CD) have been obtained. Our proposedsystem shows nearly 16\% improvement over the existing state-of-the-art result.

Seek and You Will Find: A New Optimized Framework for Efficient Detection of Pedestrian