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.

本文介绍了一种基于仿生学模型的视觉跟踪方法，该方法模拟大脑视皮层的视觉认知机制，采用浅层神经元和高级学习机制，加速了实时学习和检测，实验证明该方法具有较高的效率、精度和鲁棒性。

BIT：生物启示追踪器

BIT: Biologically Inspired Tracker

The human visual system is an intricate network of brain regions that enables
us to recognize the world around us. Despite its abundant lateral and feedback
connections, object processing is commonly viewed and studied as a feedforward
process. Here, we measure and model the rapid representational dynamics across
multiple stages of the human ventral stream using time-resolved brain imaging
and deep learning. We observe substantial representational transformations
during the first 300 ms of processing within and across ventral-stream regions.
Categorical divisions emerge in sequence, cascading forward and in reverse
across regions, and Granger causality analysis suggests bidirectional
information flow between regions. Finally, recurrent deep neural network models
clearly outperform parameter-matched feedforward models in terms of their
ability to capture the multi-region cortical dynamics. Targeted virtual cooling
experiments on the recurrent deep network models further substantiate the
importance of their lateral and top-down connections. These results establish
that recurrent models are required to understand information processing in the
human ventral stream.

本研究采用时间分辨脑成像和深度学习的方法，探究人脑视觉处理的层次动态及信息流向，结果表明，回归深度神经网络模型比参数匹配前馈模型更能准确捕捉人脑视觉处理的多区域功能