  Visual tracking is challenging due to image variations caused by various
factors, such as object deformation, scale change, illumination change and
occlusion. Given the superior tracking performance of human visual system
(HVS), an ideal design of biologically inspired model is expected to improve
computer visual tracking. This is however a difficult task due to the
incomplete understanding of neurons' working mechanism in HVS. This paper aims
to address this challenge based on the analysis of visual cognitive mechanism
of the ventral stream in the visual cortex, which simulates shallow neurons (S1
units and C1 units) to extract low-level biologically inspired features for the
target appearance and imitates an advanced learning mechanism (S2 units and C2
units) to combine generative and discriminative models for target location. In
addition, fast Gabor approximation (FGA) and fast Fourier transform (FFT) are
adopted for real-time learning and detection in this framework. Extensive
experiments on large-scale benchmark datasets show that the proposed
biologically inspired tracker performs favorably against state-of-the-art
methods in terms of efficiency, accuracy, and robustness. The acceleration
technique in particular ensures that BIT maintains a speed of approximately 45
frames per second.
