Research On Multi-Stage Object Tracking With Siamese Anchor-Free Proposal Network

As one of the important directions in the field of computer vision,target tracking technology is widely used in video surveillance,intelligent transportation,autonomous driving,and humancomputer interaction systems.Given the initial state of the tracked object,visual target tracking aims to accurately estimate the dynamic information such as the specific position,scale change,and action trajectory of the target object in continuous video frames.However,due to the wide variety of tracking scenes,the complex and changeable environment,and the complex conditions such as deformation,occlusion,blurring,and rapid movement caused by target motion,it is still considered a challenging task to establish an accurate,robust and real-time target tracking system.In this paper,we propose an efficient framework which can be end-to-end trained offline and meet the real-time requirement: Multi-Stage Visual Tracking with Siamese Anchor-Free Proposal Network(MS-Siam AFPN).The algorithm is a three-stage siamese network tracker composed of feature extraction and fusion sub-network(EF),classification and regression sub-network(CR),verification and regression(VR)sub-network.First,the Anchor-Free strategy is introduced in the CR stage,which can make full use of positive and negative samples for training while reducing neural network parameters.Secondly,in the VR stage,aim to achieve better robustness and recognizability.On the one hand,a feature purification module is designed,which uses the channel attention mechanism and the deformable ROI pooling operation to jointly process the input fusion features,so as to expand the feature receptive field and strengthen the characterization ability of image features;on the other hand,the target recognition and position regression are regarded as different processing tasks,and the recognition score and position fine-tuning of candidate targets are obtained by designing two independent network branches,thereby avoiding feature ambiguity.Due to the synergy of the above three innovations,MS-Siam AFPN has achieved a larger performance improvement compared with SPM-Tracker,and achieved SOTA performance in otb-50,otb-100,vot-2016,lasot and uav123 public dataset benchmarks.