# Adaptive Frame Sampling and Feature Alignment for Multi-Frame Infrared Small Target Detection
## 2 Related Work
### Infrared Small Target Detection Based on Multiple Frames  
The infrared small target detection method based on sequence images mainly uses the temporal and spatial information of the sequence images, combined with the continuity of the target motion and the consistency of the trajectory to detect the target.  
In 2011, [PERSON] et al. [25] proposed two improved optical flow estimation methods. By modifying the constraint function and introducing an adaptive threshold, they solved the problems of the accuracy of optical flow estimation and holes in the target area at low gradient points. In 2019, [PERSON] et al. [26] proposed a sparse representation method based on online learning of double sparse background dictionaries for efficient detection of small infrared targets. This method achieved accurate target detection and background clutter suppression by constructing a background dictionary and using a sparse representation model to decompose the target image. In 2020, [PERSON] et al. [27] proposed an infrared moving small target detection algorithm that utilizes the spatiotemporal consistency of motion trajectories. By densely sampling and tracking feature points, dense trajectories are calculated, and suspicious trajectories are deleted using the motion characteristics of the target. A binary image is created based on the image coordinates of the trajectory points, extracting significant contours as candidate target areas. Through an encoding mechanism of contour numbers, the temporal consistency of contour codewords is used to distinguish moving targets and backgrounds. In 2021, [PERSON] et al. [28] proposed a new small target detection method based on the spatiotemporal information of infrared images. By establishing a non-overlapping patch spatio-temporal tensor (NPSTT) model and introducing a tensor capped kernel norm (TCNN), potential target detection in complex backgrounds is achieved.  
However, the relevant research on applying deep learning technology to multi-frame infrared small target detection is still insufficient. [PERSON] et al. [29] directly detected video frames using deep learning single-frame detectors, which cannot utilize temporal context information. Some methods use the spatiotemporal context information of the current frame and super-resolution operations to enhance the details of the current frame, to improve target recognition capabilities [30; 31]. [PERSON] et al. [32] used a maximum filter preprocessing operation to integrate multiple frames as input to a single-frame detection method. [PERSON] et al. [33] used the frame to be detected as the central frame, performed a subtraction operation between the adjacent frames adapted to the central frame and the central frame to obtain a difference map, and concatenated the central frame with the two obtained difference maps before inputting it into the detection network. These methods utilize multiple frames more directly and do not take into account the redundancy of consecutive frames. At the same time, they do not fully consider the interference caused by the background motion of infrared video frames on detection. On this basis, this paper proposes an adaptive frame sampling method and a multi-frame detection network aligned at the feature map level.