Abstract: Target detection and tracking technology have been widely used in the fields of transportation, medical, safety and military affairs, etc. However, there still exist some challenges in target detection and tracking, such as dim small target, complex background, target occlusion, and appearance changes, etc. On the other hand, as the most effective biointelligence system, Human Visual System has significant advantages in image processing. In this paper, combining neural engineeringoriented brainlike models and computer engineeringoriented DNNs, three target detection and tracking algorithms based on braininspired models and DNNs are proposed, including: a moving target detection algorithm based on Algorithmic Lateral Inhibition (ALI) model, a dim target detection algorithm based on StructureContrast Visual Attention model, and a target tracking algorithm based on memory mechanism and convolutional feature. The comparison experiments show that applying the braininspired models and DNNs to the infrared target detection and tracking is beneficial to achieve accurate target detection and robust tracking under complex conditions.

Key words: human visual system, deep neural network, braininspired model, target detection and tracking