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

Person re-identification is vital for monitoring and tracking crowd movementto enhance public security. However, re-identification in the presence ofocclusion substantially reduces the performance of existing systems and is achallenging area. In this work, we propose a plausible solution to this problemby developing effective occlusion detection and reconstruction framework forRGB images/videos consisting of Deep Neural Networks. Specifically, a CNN-basedocclusion detection model classifies individual input frames, followed by aConv-LSTM and Autoencoder to reconstruct the occluded pixels corresponding tothe occluded frames for sequential (video) and non-sequential (image) data,respectively. The quality of the reconstructed RGB frames is further refinedand fine-tuned using a Conditional Generative Adversarial Network (cGAN). Ourmethod is evaluated on four well-known public data sets of the domain, and thequalitative reconstruction results are indeed appealing. Quantitativeevaluation in terms of re-identification accuracy of the Siamese network showedan exceptional Rank-1 accuracy after occluded pixel reconstruction on variousdatasets. A comparative analysis with state-of-the-art approaches alsodemonstrates the robustness of our work for use in real-life surveillancesystems.

A Novel end-to-end Framework for Occluded Pixel Reconstruction with Spatio-temporal Features for Improved Person Re-identification