Dense Feature Aggregation and Pruning for RGBT Tracking

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Summary

This paper presents a novel deep fusion algorithm based on the representations from an end-to-end trained convolutional neural network that achieves clear state-of-the-art against other RGB and RGBT tracking methods.

Type
article
Published
2019-07-24
Cited by
218
References
47
Access
Open access

Keywords

Boosting (machine learning), Convolutional neural network, Complementarity (molecular biology), Pooling, Pattern recognition (psychology)

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