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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Sparse representation has been widely studied in visual tracking, which hasshown promising tracking performance. Despite a lot of progress, the visualtracking problem is still a challenging task due to appearance variations overtime. In this paper, we propose a novel sparse tracking algorithm that welladdresses temporal appearance changes, by enforcing template representabilityand temporal consistency (TRAC). By modeling temporal consistency, ouralgorithm addresses the issue of drifting away from a tracking target. Byexploring the templates' long-term-short-term representability, the proposedmethod adaptively updates the dictionary using the most descriptive templates,which significantly improves the robustness to target appearance changes. Wecompare our TRAC algorithm against the state-of-the-art approaches on 12challenging benchmark image sequences. Both qualitative and quantitativeresults demonstrate that our algorithm significantly outperforms previousstate-of-the-art trackers.

Enforcing Template Representability and Temporal Consistency for Adaptive Sparse Tracking