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

Variations of target appearance such as deformations, illumination variance,occlusion, etc., are the major challenges of visual object tracking thatnegatively impact the performance of a tracker. An effective method to tacklethese challenges is template update, which updates the template to reflect thechange of appearance in the target object during tracking. However, withtemplate updates, inadequate quality of new templates or inappropriate timingof updates may induce a model drift problem, which severely degrades thetracking performance. Here, we propose BackTrack, a robust and reliable methodto quantify the confidence of the candidate template by backward tracking it onthe past frames. Based on the confidence score of candidates from BackTrack, wecan update the template with a reliable candidate at the right time whilerejecting unreliable candidates. BackTrack is a generic template update schemeand is applicable to any template-based trackers. Extensive experiments onvarious tracking benchmarks verify the effectiveness of BackTrack over existingtemplate update algorithms, as it achieves SOTA performance on various trackingbenchmarks.

BackTrack: Robust template update via Backward Tracking of candidate template