Improved SiamFC Target Tracking Algorithm Based on Anti-Interference Module

Yejin Yan,Jiayu Ou,Zhifeng Liu,Wenxiao Huo,Tianping Li,Chao Wang

doi:10.1155/2022/2804114

Abstract

The SiamFC target tracking algorithm has attracted extensive attention because of its good balance between speed and performance, but the tracking effect of the SiamFC algorithm is not satisfactory in complex background scenes. When SiamFC algorithm uses deep semantic features for tracking, it has good recognition ability for different types of objects, but it has insufficient discrimination for the same types of objects. Therefore, we propose an effective anti-interference module to improve the discrimination ability of the algorithm. The anti-interference module uses another feature extraction network to extract the features of the candidate target images generated by the SiamFC main network. In addition, we set up the feature vector set to save the feature vectors of the tracking target and the template image. Finally, the tracking target is selected by calculating the minimum cosine distance between the feature vector of the candidate target and the vector in the feature vector set. A large number of experiments show that our anti-interference module can effectively improve the performance of SiamFC algorithm, and the performance of this algorithm can be comparable to the popular algorithms.

Highlights

The field of computer vision has advanced rapidly in recent years, and the direction of target tracking has become a research hotspot for many research institutions and universities
Many problems still exist in the field of target tracking, such as complex background, target occlusion, and scale change [4]
The other category is based on a non-Siamese network [14,15,16,17,18], which is mostly studied using correlation filter (CF) [19,20,21,22]; because algorithms in this category are constantly improving, their tracking speed and performance based on CF cannot be well balanced

Summary

Introduction

The field of computer vision has advanced rapidly in recent years, and the direction of target tracking has become a research hotspot for many research institutions and universities. The majority of researchers prefer a Siamese network-based target tracking algorithm, and its classical algorithm SiamFC [5] has become a milestone algorithm It can effectively balance the speed and accuracy of target tracking and has become the cornerstone of many subsequent improved algorithms. In the complex background, the response value of the target is close to the interference target, and even the response value of the interference target is higher than that of the tracking target, which will inevitably affect the tracking effect Based on this background, this study proposes an antiinterference module and designs an appearance feature extraction network. This study proposes an antiinterference module and designs an appearance feature extraction network It extracts features of the tracking target in recent and initial frames and extracts features of the candidate target in the current frame. Multiple candidate boxes are extracted on the basis of SiamFC, and the candidate boxes are input into the appearance feature extraction network to obtain the correlation vector (3) The feature vector set is designed, which can save the tracking target feature vector in recent frames and the template image (4) The cosine distance between the vector in the feature vector set and the feature vector of the candidate target is calculated to determine the tracking target, which solves the disadvantage that only template image features can be used in SiamFC algorithm and improves the performance of the algorithm for long-time tracking

Related Works

The Proposed Algorithm

Experiments

Findings

Conclusions