High-Performance Visual Tracking Based on High-Order Pooling Network

Convolution Neural Network (CNN) features have been widely used in visual tracking due to their powerful representation. As an important component of CNN, the pooling layer plays a critical role, but the max/average/min operation only explores the first-order information, which limits the discrimina...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.1-1
Main Authors: Feng, Xinxi, Pu, Lei
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Convolutional neural networks
Correlation
Correlation Filter
Data models
Deep Features
Feature extraction
Feature maps
High-order Pooling Network
Model Fusion
Optical tracking
Representations
Target tracking
Tracking
Visual systems
Visual Tracking
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Convolution Neural Network (CNN) features have been widely used in visual tracking due to their powerful representation. As an important component of CNN, the pooling layer plays a critical role, but the max/average/min operation only explores the first-order information, which limits the discrimination ability of the CNN features in some complex situations. In this paper, a high-order pooling layer is integrated into the VGG16 network for visual tracking. In detail, a high-order covariance pooling layer is employed to replace the last maxpooling layer to learn discrimination features and is trained on the ImageNet and CUB200-2011 data sets. In tracking stage, the multiple levels of feature maps are extracted as the appearance representation of the target. After that, the extracted CNN features are integrated into the correlation filters framework when tracking is on-the-fly. The experimental results show that the proposed algorithm achieves excellent performance in both success rate and tracking accuracy.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3208579