NDLI: Occlusion-robust object tracking based on the confidence of online selected hierarchical features

Content Provider	IET Digital Library
Author	Liu, Mingjie Jin, Cheng Bin Yang, Bin Cui, Xuenan Kim, Hakil
Abstract	In recent years, convolutional neural networks (CNNs) have been widely used for visual object tracking, especially in combination with correlation filters (CFs). However, the increasing complex CNN models introduce more useless information, which may decrease the tracking performance. This study proposes an online feature map selection method to remove noisy and irrelevant feature maps from different convolutional layers of CNN, which can reduce computation redundancy and improve tracking accuracy. Furthermore, a novel appearance model update strategy, which exploits the feedback from the peak value of response maps, is developed to avoid model corruption. Finally, an extensive evaluation of the proposed method was conducted over OTB-2013 and OTB-2015 datasets, and compared with different kinds of trackers, including deep learning-based trackers and CF-based trackers. The results demonstrate that the proposed method achieves a highly satisfactory performance.
Starting Page	2023
Ending Page	2029
Page Count	7
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 11, Nov (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5454
Journal	IET Image Processing
Publisher Date	2018-07-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Appearance Model Update Strategy CF-based Trackers CNN Computation Redundancy Computer Vision And Image Processing Technique Convolutional Layer Convolutional Neural Network Correlation Filter Deep Learning-based Trackers Feature Extraction Feature Map Selection Method Learning in AI Model Corruption Neural Computing Technique Neural Nets Object Detection Object Tracking Occlusion-robust Object Tracking Online Selected Hierarchical Feature Optical, Image And Video Signal Processing OTB-2015 Datasets Tracking Accuracy Tracking Performance Useless Information Visual Object Tracking
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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