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.

Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion.

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.