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Fully convolutional adaptive tracker with real time performance
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Paper Abstract

We present a Fully Convolutional Adaptive Tracker (FCAT) based on a Siamese architecture that operates in real-time and is well suited for tracking from aerial platforms. Real time performance is achieved by using a fully convolutional network to generate a densely sampled response map in a single pass. The network is fined-tuned on the tracked target with an adaptation approach that is similar to the procedure used to train Discriminative Correlation Filters. A key difference between FCAT and Discriminative Correlation Filters is that FCAT fine-tunes the template feature directly using Stochastic Gradient Descent while DCF regresses a correlation filter. The effectiveness of the proposed method was illustrated on surveillance style videos, where FCAT performs competitively with state-of-the-art visual trackers while maintaining real-time tracking speeds of over 30 frames per second.

Paper Details

Date Published: 13 May 2019
PDF: 9 pages
Proc. SPIE 10992, Geospatial Informatics IX, 1099204 (13 May 2019); doi: 10.1117/12.2518823
Show Author Affiliations
Breton Minnehan, Rochester Institute of Technology (United States)
Abu Md Niamul Taufique, Rochester Institute of Technology (United States)
Andreas Savakis, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 10992:
Geospatial Informatics IX
Kannappan Palaniappan; Peter J. Doucette; Gunasekaran Seetharaman, Editor(s)

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