Fully convolutional adaptive tracker with real time performance
Breton Minnehan, Abu Md Niamul Taufique, Andreas E. Savakis · 2019
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.