Fully convolutional adaptive tracker with real time performance

Breton Minnehan; Abu Md Niamul Taufique; Andreas Savakis

doi:10.1117/12.2518823

13 May 2019 Fully convolutional adaptive tracker with real time performance

Breton Minnehan, Abu Md Niamul Taufique, Andreas Savakis

Proceedings Volume 10992, Geospatial Informatics IX; 1099204 (2019) https://doi.org/10.1117/12.2518823
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Citation Download Citation

Breton Minnehan, Abu Md Niamul Taufique, and Andreas Savakis "Fully convolutional adaptive tracker with real time performance", Proc. SPIE 10992, Geospatial Informatics IX, 1099204 (13 May 2019); https://doi.org/10.1117/12.2518823

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