Multi-Stage Visual Tracking with Siamese Anchor-Free Proposal Network

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

View graph of relations

Author(s)

  • Guang Han
  • Jinpeng Su
  • Yaoming Liu
  • Yuqiu Zhao
  • Sam Kwong

Related Research Unit(s)

Detail(s)

Original languageEnglish
Number of pages14
Journal / PublicationIEEE Transactions on Multimedia
Online published15 Nov 2021
Publication statusOnline published - 15 Nov 2021

Abstract

The austere challenge of visual object tracking is to find the target to be tracked in various noise interference and obtain its accurate bounding box coordinates. Recently, the object tracking technology based on the Siamese network has made great breakthroughs, and more and more Siamese network trackers have been proposed with superior performance. They still have some shortcomings. To this end, a new Multi-Stage visual tracking algorithm with Siamese Anchor-Free Proposal Network (MS-SiamAFPN) is proposed in this paper. The algorithm is a three-stage Siamese network tracker composed of Feature Extraction and Fusion (FEF) sub-network, Classification and Regression (CR) sub-network, Validation and Regression (VR) sub-network in series. Firstly, the Anchor-Free Proposal Network (AFPN) module is designed in the CR stage, which can make full use of positive and negative samples for training while reducing neural network parameters. Secondly, aim to achieve better robustness and recognizability in the VR stage, on the one hand, a novel Feature Purification (FP) module is designed, which can automatically select the important channels, and extract the features of irregular regions on the input fusion features, so as to strengthen the representation 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 newly designing the Dual-Branch Network (DBN) structure, thereby avoiding feature ambiguity. Due to the synergy of the above these innovations, MS-SiamAFPN has obtained a large performance improvement, and achieved SOTA performance in multiple public dataset benchmarks.

Research Area(s)

  • anchor-free, Deep learning, Feature extraction, feature purification, Interference, Object tracking, Siamese network, Target tracking, Task analysis, Training