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Proceedings Paper

Similarity measures for target tracking with aerial images
Author(s): Jenfeng Sam Li; Igor Ternovskiy; James Graham
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Paper Abstract

A Self-structuring Data Learning Algorithm was introduced and has been implemented in our prior work. While the algorithm and the software package are advancing, it has been tested with both synthetic data and real-world data. After encouraging synthetic data test results, real-world data testing also shows promising outcomes while posing some challenges such as object occlusion, objects merging, and going into and emerging from under bridge. To resolve such problems, a multi-int solution is proposed. One of the key features in this solution is similarity measure. There are different types of similarity measures. In this paper, we primarily focus on aerial images similarity measure. The images we worked on presents unique challenge in similarity measure because of small object in distance and large area image, which consequently provides limited information. To deal with this difficulty, we have developed 14 different similarity metrics by employing Normalized Cross Correlation method, Sum of Squared Differences, and overlapping and colors of pixels. We used object tracking ability to evaluate the metrics. The simulation results show each metric has some advantages and disadvantages. In attempt to improve tracking capability, we imposed some metrics thresholds in addition to the image similarity metrics. Such metrics thresholds were learned from labeled data with valuation of tracking correctness. To further enhance tracking ability, speed similarity was incorporated on top of two features mentioned above. More improvement can be done by studying robustness of images similarity metrics and using tracks fusion.

Paper Details

Date Published: 15 May 2018
PDF: 15 pages
Proc. SPIE 10630, Cyber Sensing 2018, 106300O (15 May 2018); doi: 10.1117/12.2310001
Show Author Affiliations
Jenfeng Sam Li, Georgia Institute of Technology (United States)
Wright State Univ. (United States)
Riverside Research (United States)
Igor Ternovskiy, Air Force Research Lab. (United States)
James Graham, Riverside Research (United States)

Published in SPIE Proceedings Vol. 10630:
Cyber Sensing 2018
Igor V. Ternovskiy; Peter Chin, Editor(s)

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