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

Multi-Camera Multi-Object Tracking (MC-MOT) utilizes information frommultiple views to better handle problems with occlusion and crowded scenes.Recently, the use of graph-based approaches to solve tracking problems hasbecome very popular. However, many current graph-based methods do noteffectively utilize information regarding spatial and temporal consistency.Instead, they rely on single-camera trackers as input, which are prone tofragmentation and ID switch errors. In this paper, we propose a novelreconfigurable graph model that first associates all detected objects acrosscameras spatially before reconfiguring it into a temporal graph for TemporalAssociation. This two-stage association approach enables us to extract robustspatial and temporal-aware features and address the problem with fragmentedtracklets. Furthermore, our model is designed for online tracking, making itsuitable for real-world applications. Experimental results show that theproposed graph model is able to extract more discriminating features for objecttracking, and our model achieves state-of-the-art performance on several publicdatasets.

ReST: A Reconfigurable Spatial-Temporal Graph Model for Multi-Camera Multi-Object Tracking