Kernel Correlation Filter Algorithm and Its Research in Small Target and Occlusion Fields

This paper focuses on the improvement of kernel filtering algorithms and their applications, particularly on enhancing the accuracy of target recognition and tracking in modern intelligent environmental monitoring. The Kernel Correlation Filter (KCF) algorithm, due to its efficiency, high precision, and strong robustness, holds significant potential for application in surveillance environments. However, the performance of the KCF algorithm is limited in complex scenarios such as occlusion, scale changes, similar targets, and rapid movements.To address these issues, this paper proposes two improved KCF algorithms. Firstly, a scale pool algorithm is introduced, which enhances the algorithm's adaptability to target scale changes by performing multi-scale feature extraction and matching, thereby improving tracking accuracy, efficiency, and speed. Experiments demonstrate that the tracking accuracy of the improved algorithm is significantly enhanced on the OTB50 and OTB100 datasets. In experiments using a self-constructed dataset, the success rates for feature extraction, scale changes, and sampling rates were improved by 40%, 13.6%, and 29.76%, respectively.Secondly, to address the occlusion problem, an improved joint tracking algorithm combining the modified KCF with ByteTrack is proposed. The high-quality detection results of the improved KCF are utilized as input for ByteTrack, which optimizes the handling of occluded objects through its multiple matching strategies. The joint algorithm exhibits excellent performance on the OTB50 and OTB100 datasets. In the self-constructed dataset, the success rate for handling occlusion scenarios is improved by 50% compared to the original KCF, effectively solving issues such as target template updating, feature changes, and target disappearance.