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

Vision based human detection continuously attracts research interest since it is a topic of practical significance. The well-established Histogram of Oriented Gradients (HOG) human detector, though regarded as a reference for human detection, still suffers from the typical problem of the trade-off between precision and recall, relying on the threshold of its classifiers. In this paper, we propose a human detection system which can provide both good precision and recall without the need for adjusting the classification thresholds. Our strategy is to combine the HOG detector with a body part model in order to eliminate the false detections that do not match the human silhouette (body) model. For this purpose, a probabilistic model of the human body is learned to describe the relative position between the distinctive body parts. A HOG detection would be retained if the body parts can be detected in the confidence areas provided by the learned body model. Moreover, the body parts detectors are boosted cascade classifier learned with the Haar, HOG or LBP features. The multi-modal feature representation of the different human body parts is more robust against variations in human appearances. Experiment results on the INRIA data sets show that our human detector achieves a precision of 70% at a recall of 50%, which cannot be achieved by the HOG detector under any parameter settings.

Human Detection from Ground Truth Cameras through Combined Use of Histogram of Oriented Gradients and Body Part Models