Fast Fourier Transform Networks for Object Tracking Based on Correlation Filter

In this paper, we propose fast Fourier transform networks for object tracking, called FFTNet. FFTNet is a correlation filter (CF)-based tracker that integrates two main components of CF, i.e., auto correlation and cross correlation between the features of two images. Thus, FFTNet takes full advantage of CF: 1) Auto correlation and cross correlation of CF are efficiently computed by FFT so that FFTNet achieves high computational efficiency and 2) FFTNet successfully performs displacement detection even in similar signals so that it achieves good tracking performance. Moreover, FFTNet combines the advantage of CF with convolutional neural networks so that it has strong capabilities of learning feature representation and matching function. FFTNet is trained end-to-end in an off-line manner. First, we input two-input patches of target object and search region into shared convolutional layers to get their features. Then, we calculate auto correlation and cross correlation from two features. Next, we concatenate the results of auto correlation and cross correlation, and put them into another subnetwork to learn a matching function. Finally, we get a response map whose values represent the probability of each pixel belonging to the target. Experimental results demonstrate that FFTNet outperforms state-of-the-arts in both tracking accuracy and computational efficiency.