# Retina-like Imaging and Its Applications: A Brief Review
## 3 Applications
### Target Tracking  
The retina-like mechanism has a notable tracking effect, which is also suitable for the field of computer vision [46; 47]. [PERSON] et al. [48] introduced LPT into the target tracking field. They used the scale transformation characteristics of LPT for scale estimation without the requirement for multi-resolution pyramids. This method solves the problem of poor tracking performance for objects with obvious scale or appearance transformations under the Cartesian coordinates. Similarly, the high-resolution feature of the fovea that is similar to the human eye has also been widely used in the task of target grasping. [PERSON] et al. [49] used a mechanism of high resolution in fovea combined with low resolution in the periphery. This method separates the target that needs to be grasped from the complex background, which can better eliminate the effect of the rotation [50].  
Several works have also been conducted to enhance the robustness in target tracking. The general approach is the correlated filter tracking. The conventional correlation filter-based tracking approaches only track targets with boxes parallel to the coordinate axis. Similarity transformation for rotated targets has been rarely explained. Unlike conventional methods, [PERSON] et al. [51] converted the 4-degree of freedom (DoF) problem of target tracking into the 2-DoF problem. They applied LPT in correlated filter tracking and proposed a target tracking algorithm for large displacements. This method is beneficial to improve the robustness of target tracking.  
[PERSON] et al. [52] introduced a novel approach that combined scale-invariance feature transform (SIFT) and LPT. SIFT solves the problem of keypoint matching between frames, and LPT eliminates the error effect caused by rotation. This kind of stability requirement is also important in the recognition of traffic signs. [PERSON] et al. [53] used LPT to recognize traffic signs. They first used the mean shift clustering method to preprocess the road sign images and then used the random forest for classifying the target. Finally, they used a combination of LPT and cross-correlation for identification. The addition of LPT effectively reduces the detection error of road signs and decreases the effect of reduced accuracy  
Figure 8: (**a**) cvrDEM, (**b**) enlarged view of the area outlined by a rectangle in (**a**), (**c**) grid DEM, and (**d**) enlarged view of the area outlined in (**e**).  
caused by rotation. The approach proposed by [PERSON] et al. [54] was similar to that of [PERSON] et al. They first selected ROI and then used LPT on the target in the ROI. Finally, they applied SVM classification on the fovea target. The overview of this approach is shown in Figure 9.  
The retina-like mechanism also plays an important role for fast-moving objects. High-speed target detection is required in various scenarios, such as sports events and traffic detection. Conventional cameras generally record visual information during the entire exposure time through the shutter. By contrast, [PERSON] et al. [55] proposed a spike camera method based on the retina-like mechanism. This method continuously monitors the light intensity value of fast-moving targets and records the continuous spikes emitted by each pixel. By performing independent operations on each spike sequence, they improved the effect of frame loss caused by high-speed moving targets within the exposure time. On this basis, [PERSON] et al. [56] proposed a different spike camera that combines dynamic and static information. They used the retina-like principle to record continuous spike data and reconstruct images. This structure not only accurately reconstructs static targets but also has a good capture effect on high-speed moving targets. They also constructed a new spike dataset to provide a basis for subsequent research.