NDLI: Correlation, Kalman filter and adaptive fast mean shift based heuristic approach for robust visual tracking

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Correlation, Kalman filter and adaptive fast mean shift based heuristic approach for robust visual tracking

Content Provider	Springer Nature Link
Author	Ali, Ahmad Jalil, Abdul Ahmed, Javed Iftikhar, Muhammad Aksam Hussain, Mutawarra
Copyright Year	2014
Abstract	Correlation tracker is computation intensive (if the search space or the template is large), has template drift problem, and may fail in case of fast maneuvering target, rapid changes in its appearance, occlusion suffered by it and clutter in the scene. Kalman filter can predict the target coordinates in the next frame, if the measurement vector is supplied to it by a correlation tracker. Thus, a relatively small search space can be determined where the probability of finding the target in the next frame is high. This way, the tracker can become fast and reject the clutter, which is outside the search space in the scene. However, if the tracker provides wrong measurement vector due to the clutter or the occlusion inside the search region, the efficacy of the filter is significantly deteriorated. Mean-shift tracker is fast and has shown good tracking results in the literature, but it may fail when the histograms of the target and the candidate region in the scene are similar (even when their appearance is different). In order to make the overall visual tracking framework robust to the mentioned problems, we propose to combine the three approaches heuristically, so that they may support each other for better tracking results. Furthermore, we present novel method for (1) appearance model updating which adapts the template according to rate of appearance change of target, (2) adaptive threshold for similarity measure which uses the variable threshold for each forthcoming image frame based on current frame peak similarity value, and (3) adaptive kernel size for fast mean-shift algorithm based on varying size of the target. Comparison with nine state-of-the-art tracking algorithms on eleven publically available standard dataset shows that the proposed algorithm outperforms the other algorithms in most of the cases.
Starting Page	1567
Ending Page	1585
Page Count	19
File Format	PDF
ISSN	18631703
Journal	Signal, Image and Video Processing
Volume Number	9
Issue Number	7
e-ISSN	18631711
Language	English
Publisher	Springer London
Publisher Date	2014-01-31
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Object tracking Template drift Clutter Occlusion Fast mean shift Kalman filter Normalized correlation Signal, Image and Speech Processing Image Processing and Computer Vision Computer Imaging, Vision, Pattern Recognition and Graphics Multimedia Information Systems
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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