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Dictionary learning and confidence map estimation-based tracker for robot-assisted therapy system

Research output: Chapter in Book/Report/Conference proceedingConference contribution

In this paper, we propose a new tracker based on dictionary learning and confidence map estimation for a robot-assisted therapy system. We first over-segment the image into superpixel patches, and then employ color and depth cues to estimate the object confidence of each superpixel patch. We build two Bag-of-Word (BoW) models from initial frames to encode foreground/background appearance, and compute object confidence at superpixel level using BoW model in both foreground and background. We further refine target confidence by depth-based statistical features to mitigate noise interference and the uncertainty of visual cues. We derive the global confidence of each target candidate at bag level, and incorporate the confidence estimations to determine the posterior probability of each candidate within the Bayesian framework. Experimental results demonstrate the superior performance of the proposed method, especially in long-term tracking and occlusion handling.
Original languageEnglish
Title of host publicationPattern Recognition and Computer Vision
Subtitle of host publicationSecond Chinese Conference, PRCV 2019, Xi’an, China, November 8–11, 2019, Proceedings, Part I
EditorsZhouchen Lin, Liang Wang, Jian Yang, Guangming Shi, Tieniu Tan, Nanning Zheng, Xilin Chen, Yanning Zhang
Number of pages13
ISBN (Electronic)978-3-030-31654-9
ISBN (Print)978-3-030-31653-2
Publication statusPublished - 31 Oct 2019
EventPRCV 2019: Chinese Conference on Pattern Recognition and Computer Vision - Xi'an, China
Duration: 8 Nov 201911 Nov 2019

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
NameImage Processing, Computer Vision, Pattern Recognition, and Graphics


ConferencePRCV 2019: Chinese Conference on Pattern Recognition and Computer Vision

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