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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Traditionally ,pedestrian counting is a manual process. It requires a lot of manpower and material resources, and also generates the possible human error. Therefore, the reality in many cases are in urgent need of automatic pedestrian counting. The most widely deployed methods utilize laser sensors and infrared sensors. However, these methods sometimes fail to count pedestrians correctly when the heights of pedestrians walking together are similar or when the heights do not fall within the presumed range, because such methods depend on the difference in propagation delays of reflected laser pulses or infrared light. Although the methods using multiple infrared sensors can count pedestrians moving various directions, the counting accuracy degrades considerably when the street has much traffic and occlusion occurs frequently. An occlusion is caused by pedestrians interacting with each other when many pedestrians are present. In this paper, we introduce a method based on Pyramid optical flows clustering of corner point to improve the counting accuracy. We also report that using length clustering, angle clustering and original location clustering to enhance the counting accuracy.

Optical flows Clustering Used for Counting Pedestrians