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

Multiple human tracking is a fundamental problem for scene understanding.Although both accuracy and speed are required in real-world applications,recent tracking methods based on deep learning have focused on accuracy andrequire substantial running time. This study aims to improve running speed byperforming human detection at a certain frame interval because it accounts formost of the running time. The question is how to maintain accuracy whileskipping human detection. In this paper, we propose a method that complementsthe detection results with optical flow, based on the fact that someone'sappearance does not change much between adjacent frames. To maintain thetracking accuracy, we introduce robust interest point selection within humanregions and a tracking termination metric calculated by the distribution of theinterest points. On the MOT20 dataset in the MOTChallenge, the proposedSDOF-Tracker achieved the best performance in terms of the total running speedwhile maintaining the MOTA metric. Our code is available athttps://github.com/hitottiez/sdof-tracker.

SDOF-Tracker: Fast and Accurate Multiple Human Tracking by Skipped-Detection and Optical-Flow