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

Object detection serves as a significant step in improving performance ofcomplex downstream computer vision tasks. It has been extensively studied formany years now and current state-of-the-art 2D object detection techniquesproffer superlative results even in complex images. In this chapter, we discussthe geometry-based pioneering works in object detection, followed by the recentbreakthroughs that employ deep learning. Some of these use a monolithicarchitecture that takes a RGB image as input and passes it to a feed-forwardConvNet or vision Transformer. These methods, thereby predict class-probabilityand bounding-box coordinates, all in a single unified pipeline. Two-stagearchitectures on the other hand, first generate region proposals and then feedit to a CNN to extract features and predict object category and bounding-box.We also elaborate upon the applications of object detection in video eventrecognition, to achieve better fine-grained video classification performance.Further, we highlight recent datasets for 2D object detection both in imagesand videos, and present a comparative performance summary of variousstate-of-the-art object detection techniques.

Recent Trends in 2D Object Detection and Applications in Video Event Recognition