University of Oulu

X. Cheng, Y. Gu, B. Chen, Y. Zhang and J. Shi, "Weighted Multiple Instance-Based Deep Correlation Filter for Video Tracking Processing," in IEEE Access, vol. 7, pp. 161220-161230, 2019. doi: 10.1109/ACCESS.2019.2951600

Weighted multiple instance-based deep correlation filter for video tracking processing

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Author: Cheng, Xu1; Gu, Yongxiang1; Chen, Beijing1;
Organizations: 1School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
2School of Information Science and Engineering, Southeast University, Nanjing 210096, China
3Center for Machine Vision and Signal Analysis, University of Oulu, FI-90014 Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 7.3 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-02-27


With the development of internet technology, the video data has been widely used in multimedia devices, such as video surveillance, webcast, and so on. Lots of visual processing algorithms are developed to handle the corresponding visual task, but the challenging problems still exist. In this paper, we propose a weighted multiple instances based deep correlation filter for visual tracking processing, which utilizes the importance of instances for training of deep learning model and correlation filter. First, the initial object appearance is modeled based on the confidence of the object and background at the first frame. During the tracking, the superpixel is used to capture the object appearance variations. Most importantly, our tracker can enhance the discriminative ability of the object using deep residual network and improve the tracking efficiency with correlation filter. Second, we introduce the sample importance into residual deep learning model to improve the training performance. We define the importance of each instance by computing the sore of all the pixels within the corresponding instance. Third, we update the parameters of deep learning network and correlation filter in a fixed interval frames to reduce the object drift. Extensive experiments on the OTB2015 benchmark and VOT2018 dataset demonstrate that the proposed object tracking algorithm outperforms the state-of-the-art tracking algorithms.

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Series: IEEE access
ISSN: 2169-3536
ISSN-E: 2169-3536
ISSN-L: 2169-3536
Volume: 7
Pages: 161220 - 161230
DOI: 10.1109/ACCESS.2019.2951600
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Copyright information: © The Authors 2019. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see