Abstract
In order to improve the tracking accuracy and real-time performance of the optoelectronic tracking system, an improved kernelized correlation filter approach is developed to obtain precise tracking of a maneuvering object. The proposed strategy contains merits of adaptive threshold approach, kernelized correlation filter method, and Kalman filter algorithm. The adaptive threshold approach can choose the suitable threshold in accordance with the size of the target in the image to improve the tracking performance of the kernelized correlation filter method. When the change between previous position and current position is larger than the distance threshold, Kalman filter algorithm is used to predict the target position for tracking. The tracking accuracy of the proposed algorithm is improved by updating the prediction of the target position with a trusted algorithm. The experimental results on comparison with some state-of-the-art trackers, such as kernelized correlation filter; Tracking-Learning-Detection; scale adaptive with multiple features; minimum output sum of squared error; and dual correlation filter, demonstrate that the proposed approach has the effectiveness of tracking accuracy and real-time performance in tracking the maneuvering object.
Highlights
Optoelectronic tracking system has been widely used in military and civil domain in recent years.[1,2] It is a comprehensive technology including optics, mechanics, electrics, automation, and sensors
The principle of the proposed visual tracking strategy is introduced. It consists of adaptive threshold approach, Kernelized correlation filter (KCF) algorithm, and Kalman filter (KF)-based tracking with novel algorithm of position updating
The proposed algorithm integrates the merits of adaptive threshold approach, KCF algorithm, and KF algorithm
Summary
Optoelectronic tracking system has been widely used in military and civil domain in recent years.[1,2] It is a comprehensive technology including optics, mechanics, electrics, automation, and sensors. Scale adaptive with multiple features (SAMF)[27] increased scale to the KCF framework by sampling the primitive object with different scales and learning the model at each scale It integrates HOG descriptor with a color-naming[28] technique to improve the tracking performance, it leads to a large amount of computational cost. The article is organized as follows: The second section describes the proposed visual tracking strategy It contains adaptive threshold approach, KCF method and KF algorithm. The principle of the proposed visual tracking strategy is introduced It consists of adaptive threshold approach, KCF algorithm, and KF-based tracking with novel algorithm of position updating. The appropriate threshold is selected by adaptive threshold approach which utilizes the ratio M1 and M matching score instead of utilizing global value
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