Robust Collaborative Discriminative Learning for RGB-Infrared Tracking

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Summary

A novel and optimal discriminative learning framework for multi-modality tracking that is able to jointly eliminate outlier samples caused by large variations and learn discriminability-consistent features from heterogeneous modalities, and collaboratively perform modality reliability measurement and target-background separation.

Type
article
Published
2018-04-27
Cited by
75
References
42
Access
Open access

Keywords

Artificial intelligence, Discriminative model, Modality (human–computer interaction), Computer science, Computer vision

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