PAF-Tracker: a novel pre-frame auxiliary and fusion visual tracker

Wei Liang, Derui Ding*, Hui Yu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Relying on a large amount of data, recent object trackers achieve superior performance. However Siamese-like trackers expose considerable shortcomings in the case of brief occlusion. To address these shortages, the paper proposes a novel pre-frame auxiliary and fusion tracking framework. Within this framework, a retained variable is first introduced to avoid some additional twin branches while retaining the previously obtained deep features of the search frames. Based on such a variable, a pre-frame auxiliary module is constructed to establish the relationship between encoding features and the retained pre-frame information and a decoding fusion module is designed to fuse the generated similarity relationship. Moreover, the Efficient IoU (EIoU) loss is employed to increase the precision of predicted bounding boxes by adding three penalty terms for the differences in the center point, length, and width of the two bounding boxes. Finally, the superiority over state-of-the-art methods is verified by numerous tests on visual tracking benchmarks.

Original languageEnglish
Title of host publication2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings
EditorsYannis Manolopoulos, Zhi-Hua Zhou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages2
ISBN (Electronic)9798350345032
ISBN (Print)9798350345049
DOIs
Publication statusPublished - 6 Nov 2023
Event10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023 - Thessaloniki, Greece
Duration: 9 Oct 202312 Oct 2023

Conference

Conference10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023
Country/TerritoryGreece
CityThessaloniki
Period9/10/2312/10/23

Keywords

  • Feature Fusion
  • Pre-Frame Auxiliary
  • Siamese Network
  • Similarity Relationship
  • Visual Tracking

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