Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87057
Title: Object Tracking Based on Stable Feature Mining Using Intraframe Clustering and Interframe Association
Authors: Lu, Hong
Gu, Ke
Lin, Weisi
Zhang, Wenjun
Keywords: Object Tracking
Stable Features Mining
Issue Date: 2017
Source: Lu, H., Gu, K., Lin, W., & Zhang, W. (2017). Object Tracking Based on Stable Feature Mining Using Intraframe Clustering and Interframe Association. IEEE Access, 5, 4690-4703.
Series/Report no.: IEEE Access
Abstract: Extracting stable features to enhance object representation has proved to be very effective in improving the performance of object tracking. To achieve this, mining techniques, such as K-means clustering and data associating, are often adopted. However, K-means clustering needs the pre-set number of clusters. Real scenarios (heavy occlusion and so on) often make the tracker lose the target object. To handle these problems, we propose an intraframe clustering and interframe association (ICIA)-based stable feature mining algorithm for object tracking. The value (in HSV space) peak contour is employed to automatically estimate the number of clusters and classify value and saturation colors of the object region to get connected subregions. Every subregion is described with observation and increment models. Multi-feature distances-based subregion association, between the current object template and the current observation, is then utilized to mine stable subregion pairs and obtain feature change ratio. Stable subregion displacements, and current detected and historical trajectories are systematically fused to locate the object. And, stable and unstable subregion features are updated separately to restrain the accumulative error. Experimental comparisons are conducted on six test sequences. Compared with several relevant state-of-the-art algorithms, the proposed ICIA tracker most accurately locates objects in four sequences and shows the second-best performance in the other two sequences with only less 1 pixel distance difference than the best method.
URI: https://hdl.handle.net/10356/87057
http://hdl.handle.net/10220/44299
DOI: 10.1109/ACCESS.2017.2673400
Rights: © 2017 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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