Infrared Small Target Tracking Based on Target Spatial Distribution With Improved Kernelized Correlation Filtering

Abstract

The application of correlation filtering in infrared small target tracking has been a mature research field. Traditionalcorrelation filtering is to describe the target features by using a single feature, which can not solve the problem of target occlusion. Because of the fast moving speed and lack of re-detection mechanism, the target tracking will produce offset, which leads to the performance of the tracker to decline. In view of the above problems, a new multi feature re detection framework is proposed for long-term tracking of small targets. The feature selects multi feature weighting function, considers the importance of intensity feature to infrared target and different regions, calculates the gray distribution weighting function of the target, and combines the weighting function into the correlation filter. Before updating the template, to verify the reliability of target detection, the average peak correlation energy is used as the confidence of candidate region. When the target is completely occluded, the prediction result of Kalman filter is used as the optimal estimation of target position in the next frame. A large number of experimental results on different video sequences show that the tracking accuracy of this method is greatly improved compared with the baseline method.

Dim and Small Target Tracking Using an Improved Particle Filter Based on Adaptive Feature Fusion

“…Recently, scholars have proposed many tracking algorithms to meet the requirements of the dim and small target tracking, which can be divided into two main categories including correlation filters-based methods [12][13][14] and PF-based methods [15][16][17][18][19]. KCF has received the most attention in the methods based on correlation filters for dim and small target tracking, which is achieved by establishing a discriminator based on the correlation operator with a kernel function.…”

“…proposed an infrared small target tracking algorithm consisting of a discriminator based on KCF and a predictor based on the least-square trajectory prediction [13], which made full use of the continuity and direction of the target motion and can robustly track the target with shot-term occlusions. Kou Z. et al proposed a method based on target spatial distribution with improved KCF for infrared small target tracking [14], which considers the importance of intensity features to infrared targets and different regions to calculate the gray distribution weighting function of the target, solving the problems of target occlusion and drift. However, the above three methods based on KCF cannot achieve a higher tracking performance on the image sequences with a fast-moving target.…”