Infrared target detection and tracking based on brain-inspired model and DNNs

Infrared target detection and tracking technology has been widely used in the fields of transportation, medical, safety and military affairs, etc. However, there stills exists some challenges in infrared 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 (HVS) has significant advantages in image processing. In this paper, several brain-inspired models (including lateral inhibition, receptive field, synchronous burst, visual attention, and cognitive memory) and Deep Neural Networks (DNNs) have been studied. Furthermore, the relevant mathematical models are established, the corresponding algorithms are proposed, and the comparison experiments are conducted. In summary, applying the brain-inspired models and DNNs to the infrared target detection and tracking is beneficial to achieve the accurate infrared target detection and robust tracking under complex conditions.