Efficient implicit fourier compression based convolutional features for visual tracking

Correlation filter has been popular in tracking due to the efficiency and robustness. Recently, tracking performance of correlation filter based trackers is significantly improved by using convolutional neural network features. However, the high dimensional feature maps lead to slow tracking speed and over-fitting. In this paper, we introduce an efficient tracking algorithm using the sparse features produced by Fourier compression to alleviate these issues. We show that Fourier compression can be implicitly conducted by discarding a large number of subproblems in our tracking framework, which is quite simple and efficient. The compression ratio of the proposed method reaches approximately 20% while maintaining the tracking accuracy. The sparse features can significantly reduce the computational complexity and over-fitting. Extensive experiments are performed in recent tracking benchmarks OTB50 and OTB100 to evaluate the proposed method. Results demonstrate that the proposed method achieves state-of-the-art tracking performance in terms of efficiency and accuracy in these benchmarks.