NDLI: Reliability verification-based convolutional neural networks for object tracking

Content Provider	IET Digital Library
Author	Hu, Xiaopeng Li, Jingting Yang, Yan Wang, Fan
Abstract	The authors propose a tracking algorithm based on the reliability analysis of the convolutional neural network to avoid drift. In general, most tracking algorithms implemented with the deep network consist of a single network; they obtain the tracking results according to the confidence and perform updates with the samples, which are collected based on the previous target state. However, this kind of algorithm relies heavily on the accuracy of tracking results, and slight deviations can lead to improperly labelled training samples and degrade the network. Therefore, they design a verification network to guarantee the reliability of the tracking network by correcting the results and it can be connected to a tracking network by sharing convolutional layers. The reliability verification network estimates the accuracy of the results of the tracking network and discards ambiguous results to avoid accumulating errors. Specifically, the verification network can distinguish the target from the confused candidates more precisely because of the optimised training data. The training samples of the verification network consist of characteristics and labels, and they are optimised by feature selection and label enhancement, respectively. The experimental results illustrate the outstanding performance compared with several state-of-the-art methods on the challenging video sequences.
Starting Page	175
Ending Page	185
Page Count	11
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 1, Jan (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5785
Journal	IET Image Processing
Publisher Date	2018-10-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Convolutional Layer Deep Network Feature Selection Feedforward Neural Network Improperly Labelled Training Sample Label Enhancement Learning in AI Neural Computing Technique Object Tracking Optical, Image And Video Signal Processing Reliability Reliability Analysis Reliability Verification-based Convolutional Neural Network Single Network Tracking Algorithm Tracking Network
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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