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

The paper presents a new method, SearchTrack, for multiple object trackingand segmentation (MOTS). To address the association problem between detectedobjects, SearchTrack proposes object-customized search and motion-awarefeatures. By maintaining a Kalman filter for each object, we encode thepredicted motion into the motion-aware feature, which includes both motion andappearance cues. For each object, a customized fully convolutional searchengine is created by SearchTrack by learning a set of weights for dynamicconvolutions specific to the object. Experiments demonstrate that ourSearchTrack method outperforms competitive methods on both MOTS and MOT tasks,particularly in terms of association accuracy. Our method achieves 71.5 HOTA(car) and 57.6 HOTA (pedestrian) on the KITTI MOTS and 53.4 HOTA on MOT17. Interms of association accuracy, our method achieves state-of-the-art performanceamong 2D online methods on the KITTI MOTS. Our code is available athttps://github.com/qa276390/SearchTrack.

SearchTrack: Multiple Object Tracking with Object-Customized Search and Motion-Aware Features