NDLI: Robust correlation filter tracking with deep semantic supervision

Content Provider	IET Digital Library
Author	Wang, Wei Chen, Zhaoming Douadji, Lyes Shi, Mingquan
Abstract	Traditional correlation filter (CF) tracking has achieved high tracking performance and speed. However, it easily falls into tracking failures in some cases of target occlusion, deformation, rotation etc. Tracking failure also contaminates the CF model and makes it less discriminative. To tackle these problems, the authors propose a deep semantic supervision tracking framework. This framework integrates the advantages of multiple features and tracking methods into an evaluation and redetection tracking mechanism. In this work, customised deep convolutional neural network (CNN) with particle filtering (PF) resampling was employed to alleviate the contamination of the CF model and improve tracking performance. The authors also adopted a mixed decision mechanism for CF tracking results evaluation. Furthermore, based on the observation that most tracking frames can be easily tracked by a CF tracker using handcrafted features, authors’ tracking method achieves real-time performance. It should be noted that the proposed framework is flexible and extensible to improve other existing trackers. In authors’ extensive experiments on large benchmark datasets including OTB2013 and OTB2015, the proposed tracker performed favourably compared to the state-of-the-art methods.
Starting Page	754
Ending Page	760
Page Count	7
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 5, Apr (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5314
Journal	IET Image Processing
Publisher Date	2019-01-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	CF Tracker Convolutional Neural Nets Deep Convolutional Neural Network Deep Semantic Supervision Tracking Framework Filtering Method in Signal Processing Handcrafted Feature High Tracking Performance Learning in AI Neural Computing Technique OTB2013 Benchmark Datasets OTB2015 Benchmark Datasets Particle Filter Particle Filtering Resampling Real-time Performance Redetection Tracking Mechanism Robust Correlation Filter Tracking Signal Processing Theory Target Occlusion Target Tracking Tracking Failure Tracking Frames
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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