基于核相关滤波和卡尔曼滤波预测的混合跟踪方法OACSTPCD

Aiming at the problem that KCF tracking algorithm might decrease the tracking performance or even tracks unsuccessfully in the occlusion scene,an anti-occlusion model adaptation image tracking algorithm was pro-posed by combining KCF and KF prediction.Firstly,considering the lack of occlusion evaluation in the traditional KCF target tracking algorithm,the peak sidelobe rate of the response map was introduced to judge the occlusion of the image target,and the occlusion types were divided into partial occlusion and severe occlusion.Then different model update strategies were adopted according to the severity of occlusion.When the target was not occluded or occluded partially,instead of using a fixed learning rate to update the model in the traditional KCF tracking algo-rithm,the target appearance model was updated by adjusting the model learning rate adaptively to avoid tracking drift.When the target was severely occluded,stopped updating the KCF model.Finally,the state space and posi-tion output models of Kalman filter were constructed by applying the motion information before severe occlusion.The Kalman filter prediction algorithm was designed to predict the moving target trajectory and estimate the target position in the occlusion scene,so as to solve the problem of target tracking failure in occlusion scenes.The OTB-2013 standard dataset was utilized to conduct extensive experiments,the results demonstrated that the distance ac-curacy of the proposed hybrid tracking algorithm KCF-KF was 0.796,and the overlap success rate was 0.692.Compared with the other traditional tracking algorithms,the tracking accuracy and success rate of the hybrid algo-rithm were better,and the hybrid algorithm could achieve better tracking performance when encountering the target occlusion challenges and solve the occlusion interference in the tracking process effectively.