Research On Multi-template Robust Target Tracking Method Based On Convolutional Network

Target tracking is one of the hotspots in the field of computer vision research.This paper first introduces the research background and development status of target tracking technology,describes two key parts of feature extraction and motion estimation in target tracking,and briefly analyzes the difficulties that the tracking process may encounter.Most of the target tracking algorithms can be divided into two steps:the target feature extraction and the algorithm that using the target feature to achieve tracking.When the complex background or target is partially occluded,the uniqueness and stability of the target feature information are reduced,so that the tracking algorithm can't distinguish the target and the background accurately,and the tracking algorithm is invalid.The target model update strategy is also an important part of the tracking algorithm,which is an indispensable part of the tracking algorithm.The current model update method relies only on the target information to which the previous frame or the nearest frame is located.The tracking history information is not fully utilized.When the occlusion and deformation occur,the tracking algorithm can't accurately reposition the target.In order to solve the above problem,this paper proposes a normalized distance weighting function and multi-template model updating strategy.The normalized weighting method constructs the weighting function through the distance from the target template pixel to the center of the template,and enhances the target feature in the complex background The multi-template model update strategy can provide a more complete target model matching information in the tracking process,this paper propose a new moving target tracking method based on the updated model combined with the convolution network.Compared with some state-of-the-art trackers in VOT2015 tracking benchmark dataset,the experiment results show that our method is robust and effective for occlusion phenomena and target deformation.