ArticleSensors (Basel, Switzerland)2023
Learning Response-Consistent and Background-Suppressed Correlation Filters for Real-Time UAV Tracking.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Hyperspectral Attention Network for Object Tracking.Sensors (Basel, Switzerland) · 2024Article
- Learning Background-Suppressed Dual-Regression Correlation Filters for Visual Tracking.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
With the advantages of discriminative correlation filter (DCF) in tracking accuracy and computational efficiency, the DCF-based methods have been widely used in the field of unmanned aerial vehicles (UAV) for target tracking. However, UAV tracking inevitably encounters various challenging scenarios, such as background clutter, similar target, partial/full occlusion, fast motion, etc. These challenges generally lead to multi-peak interferences in the response map that cause the target drift or even loss. To tackle this problem, a response-consistent and background-suppressed correlation filter is proposed for UAV tracking. First, a response-consistent module is developed, in which two response maps are generated by the filter and the features extracted from adjacent frames. Then, these two responses are kept to be consistent with the response from the previous frame. By utilizing the
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Registered trials
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