Research On Target Tracking Algorithm Based On Correlation Filtering

Target tracking is one of the important research directions in computer vision,and it is widely used in intelligent monitoring,human-computer interaction,military guidance and other fields.After decades of development,researchers have proposed many classic and excellent tracking algorithms,but due to the complexity of the environment and the variability of the target,such as lighting changes,deformation,occlusion,etc.,target tracking is still a very challenging task.In recent years,the related filtering and tracking algorithm with both accuracy and speed has attracted widespread attention of researchers,and Discriminant Scale Space Target Tracking Algorithm(DSST)is one of the representative algorithms in correlation filtering algorithms.The algorithm is improved and extended based on MOSSE.It reduces the complexity of the algorithm by Fourier transform,and introduces a three-dimensional spatial correlation filter for position and scale joint tracking,which effectively improves the tracking speed and accuracy.It is worth mentioning that the DSST algorithm code is concise,superior in performance,and has strong portability.It is worthy of further research on this basis.Therefore,this paper proposes an improved algorithm based on the DSST target tracking algorithm from the aspects of feature extraction and adaptive template update.The specific work is as follows:(1)Because the DSST algorithm uses only a single HOG feature when extracting features from an image,it can better distinguish between the target and the background when the target contour features change relatively gently,and when encountering problems such as motion blur,background interference,and planes For more complex challenges such as outer rotation,the target description capabilities of features are not sufficient.Therefore,we introduce the LBP feature and CN feature to fuse with the original HOG feature,while retaining the original good tracking method of DSST algorithm using multidimensional filter for scale estimation,and propose a multi-feature fusion target tracking algorithm based on DSST.Comparative experiments on the OTB100 public data set show that the algorithm has a certain improvement in tracking accuracy and success rate under a variety of challenges compared with the original algorithm,but the algorithm has a slower operation rate and still has a lot of room for optimization.(2)Since the DSST algorithm uses a fixed learning rate for template updating,it cannot accurately adapt to a series of changes in the target in time to accurately track the target.Therefore,we introduced a method to measure the speed of the two adjacent frames in the template update process.Kalman filtering process to make it smoother and easier to handle,and applied a scale factor as the learning rate of the position filter under the frame,so as to achieve the purpose of adaptively adjusting the template update rate.An adaptive tracking algorithm based on DSST algorithm.Comparative experiments on the OTB100 public data set show that the algorithm has a certain improvement in tracking accuracy and success rate under various challenges compared with the original algorithm,and has the potential to be transplanted to other excellent related filtering algorithms.