Dense convolutional feature histograms for robust visual object tracking

作者：

Highlights：

• Novel visual object tracking algorithm based on extracting histograms of convolutional features

• Proposed architecture is better suited to the tracking task, facilitating the training process

• Extensive experimental validation in multiple tracking benchmarks

• Entirely offline training, realtime speeds even on embedded systems

摘要

•Novel visual object tracking algorithm based on extracting histograms of convolutional features•Proposed architecture is better suited to the tracking task, facilitating the training process•Extensive experimental validation in multiple tracking benchmarks•Entirely offline training, realtime speeds even on embedded systems

论文关键词：Object Tracking,Deep Learning,Bag-of-Features,Convolutional Feature Histograms

论文评审过程：Received 2 April 2019, Revised 1 February 2020, Accepted 12 May 2020, Available online 20 May 2020, Version of Record 21 May 2020.

论文官网地址：https://doi.org/10.1016/j.imavis.2020.103933