Alpha Divergence based Siamese Network for Object Tracking

Object Tracking is a foundation in the field of vision, with powerful application capabilities. Discriminant-related tracking uses the technology of diagonalization of the circulant matrix to further improve the speed and accuracy of tracking. in the deep convolutional neural network based tracking, there is no clear explanation for the uncertainty of the target labeling. Relying heavily on manual labeling and the selection of loss function. We proposed alpha divergence-based Siamese network Tracking (alphaTK) to solve this problem. We minimized the alpha divergence between the conditional probability density output by the network and the conditional probability of annotations of the samples, void to choose the loss function. We modeled the noise generated by the label from the probabilistic perspective, and interpreting the uncertainty of the annotations. The Tracker is trained on a large number of data sets, and achieved excellent results on the OTB and UAV benchmark data sets.