IRSDT: A Framework for Infrared Small Target Tracking with Enhanced Detection

Currently, infrared small target detection and tracking under complex backgrounds remains challenging because of the low resolution of infrared images and the lack of shape and texture features in these small targets. This study proposes a framework for infrared vehicle small target detection and tracking, comprising three components: full-image object detection, cropped-image object detection and tracking, and object trajectory prediction. We designed a CNN-based real-time detection model with a high recall rate for the first component to detect potential object regions in the entire image. The KCF algorithm and the designed lightweight CNN-based target detection model, which parallelly lock on the target more precisely in the target potential area, were used in the second component. In the final component, we designed an optimized Kalman filter to estimate the target's trajectory. We validated our method on a public dataset. The results show that the proposed real-time detection and tracking framework for infrared vehicle small targets could steadily track vehicle targets and adapt well in situations such as the temporary disappearance of targets and interference from other vehicles.

Automated summary

This study proposes a framework for infrared vehicle small target detection and tracking, which consists of three components: full-image object detection, cropped-image object detection and tracking, and object trajectory prediction. The study designs a CNN-based real-time detection model with a high recall rate for the first component, and uses the KCF algorithm and a lightweight CNN-based target detection model for the second component to lock on the target more precisely. An optimized Kalman filter is designed for the final component to estimate the target's trajectory. Validation on a public dataset shows that the proposed framework can steadily track vehicle targets and adapt well in challenging situations.