Research On Video Target Tracking Algorithm Based On MDNet

With the continuous advancement of computer vision technology,target tracking technology is advancing rapidly,but in the target tracking process,factors such as background interference,occlusion,low resolution,and target shape changes will have a serious impact on the tracking results.Therefore,the research on target tracking algorithms has important practical significance.Many trackers based on Convolutional Neural Networks(CNN)have been developed.Multi-domain Convolutional Neural Network(MDNet)is an online tracking algorithm based on CNN architecture,but the tracking targets are mostly small targets.Faced with the challenges of occlusion,low resolution,and fast movement,the network cannot extract high-quality image features.In addition,MDNet treats all channels equally,contains more useless information,and uses traditional convolution methods.When faced with background clutter,scale changes,scale conversion,rotation,and aspect ratio problems,it cannot distinguish the background and the target well.Robustness is poor.This paper researches these three issues of MDNet and proposes a new tracking algorithm based on MDNet.The main research content and contributions are summarized as follows:(1)Aiming at the problem of information loss caused by convolution operation,a target tracking algorithm ac-MD combined with hole convolution is proposed.To improve the detailed information and fully extract the target features,this paper designs to remove the maximum pooling layer,and use dilated convolution to increase the receptive field of the feature map,to improve the traditional CNN network feature extraction process.(2)Aiming at the problem of the network's lack of an intermediate mechanism for processing geometric transformations,a target tracking algorithm CMDNet,which combines high-efficiency channel attention mechanism and deformable convolution,is proposed.To improve the network's attention to the target,and efficient channel attention mechanism is introduced for feature selection,and deformable convolution is designed to learn the offset information of each pixel while adding a modulation mechanism to improve tracking accuracy while avoiding banding To calculate the cost a lot,strengthen the network's modeling ability of geometric deformation.(3)Aiming at the problem of insufficient expressive ability of the network to express the target,the target tracking algorithm AE-MD which combines the efficient channel attention mechanism and the hollow convolution is proposed.MDNet simplifies the tracking problem to a binary classification problem but contains more useless background information in the learning process.To strengthen the network's ability to express the target,this paper designs the introduction of an efficient channel attention mechanism and hollow convolution to make the network focus on the target.The channel information is also enriched for target feature expression,to enhance the model's feature expression ability and strengthen the tracking network's ability to discriminate targets and backgrounds.