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Visual Object Tracking With Discriminative Filters and Siamese Networks: A Survey and Outlook.

Sajid Javed, Martin Danelljan, Fahad Shahbaz Khan

IEEE Transactions on Pattern Analysis and Machine Intelligence

|October 10, 2022

Summary

This survey reviews over 90 Discriminative Correlation Filters (DCFs) and Siamese Networks (SNs) for visual object tracking. It analyzes their performance, challenges, and offers recommendations for future research in computer vision.

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Area of Science:

Computer Vision
Machine Learning

Background:

Visual object tracking is a fundamental computer vision challenge.
Discriminative Correlation Filters (DCFs) and Siamese Networks (SNs) are leading tracking paradigms.

Purpose of the Study:

To systematically review over 90 DCF and Siamese trackers.
To analyze shared and specific research challenges in these tracking methods.
To provide recommendations for future visual object tracking research.

Main Methods:

Review of over 90 DCF and Siamese trackers.
Analysis of trackers based on results from nine tracking benchmarks.
Comparison of performance, speed, and experimental aspects across datasets and metrics.

Main Results:

Comprehensive review of DCF and Siamese tracking formulations.
Identification and analysis of open research challenges.
Performance and speed comparisons on nine benchmarks.

Conclusions:

DCFs and SNs have significantly advanced visual object tracking.
Further research is needed to address identified open challenges.
Recommendations are provided for future advancements in the field.