Biologically Inspired Tracking with Frequency Divisive Normalization Model

Biologically inspired visual object tracking has been very popular in the last decades. Several methods have exploited some visual cognitive mechanisms to outperform trackers purely based on machine vision in accuracy and speed. Although they have shown outstanding performance on recent benchmarks, they are still far from achieving the performance of a human in tracking. Visual attention is an important mechanism, which can be applied by saliency detection models. Saliency detection models can be determined in the spatial domain or the frequency domain. Spatial domain based models mostly have complex computations and are time-consuming. Thus, we have improved and recruited Frequency Divisive Normalization (FDN) model as a saliency detection method to propose a bio-inspired object tracking algorithm. This algorithm has been developed based on Fast Fourier Transform (FFT) and uses spectral features in order to achieve a high-speed tracker. It also can be launched on normal hardware. FDN model is based on Feature Integration Theory (FIT) and operates all the processes of saliency detection in the frequency domain, including decomposing the image to the features, lateral inhibition interaction, combining all features, and computing the saliency map. In addition to, extracting color, intensity and orientation it uses spatial frequency features, which is rarely used by other trackers. Extensive evaluations on large-scale benchmark datasets show that the proposed method has good performance with respect to its runtime.