Abstract：As a research hotspot in the field of computer vision，moving object tracking technique has many difficulties such as object rotation deformation，motion blur，and background clutter.Under these difficult conditions，multi-domain convolutional neural network object tracking algorithm（MDNet）frequently appears tracking failure.This paper proposes a multi-domain convolutional neural network tracking algorithm based on object segmentation，which aims to build a new network update method for MDNet networks by taking advantage of the excellent target location capabilities of segmented network.During the tracking process，the tracking failure results are corrected through object segmentation to obtain the precise position of the object.Then update MDNet network using the target frame obtained from the segmentation as a sample，which can effectively reduce the background information interference of the positive samples in the sample database，improve the classification ability of the network，and make the algorithm more robust.The proposed algorithm was tested in OTB50 and VOT2015 dataset.Compared with MDNet algorithm，the average tracking accuracy of ours is increased by 3.05% and the average success rate is increased by 2.76%.
2021, Vol. 42 
