Abstract
Considering the problems of similarity interference, partial occlusions, and changes in scale during target tracking, a target tracking method based on interference detection is proposed, which is an improvement over the Siamese fully convolutional classification and regression neural network (SiamCAR) approach. Under the proposed framework, the marginal distribution of the feature maps is used to determine the presence or absence of interferents. When interference is present in a scene, a motion vector composed of the predicted value obtained through a Kalman filter is used as the basis for target prediction. Experiments on the benchmark LaSOT dataset show that the proposed algorithm based on SiamCAR, which introduces motion features, achieves the best performance in videos with similar object interference, partial occlusions, fast motion, and small target tracking, as compared with the classical SiamCAR and other excellent target tracking algorithms.
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