# BIT: Biologically Inspired Tracker

## TL;DR

The paper introduces BIT, a biologically inspired visual tracker based on the human visual system, which combines shallow neural features with advanced learning mechanisms and acceleration techniques to achieve real-time, accurate, and robust tracking.

## Contribution

It presents a novel biologically inspired model for visual tracking that integrates neural-inspired features with efficient learning and detection methods for improved performance.

## Key findings

- Achieves approximately 45 frames per second in tracking.
- Performs favorably against state-of-the-art methods in accuracy and robustness.
- Demonstrates effectiveness on large-scale benchmark datasets.

## Abstract

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.