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SiamDF: Tracking training data-free siamese tracker.

Huayue Cai¹, Long Lan¹, Jing Zhang²

¹Institute for Quantum & State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, China.

Neural Networks : the Official Journal of the International Neural Network Society

|June 29, 2023

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This summary is machine-generated.

This study reveals that large datasets improve siamese tracking by refining target representation through background suppression. A new data-free algorithm, SiamDF, achieves strong performance without additional training data.

Area of Science:

Computer Vision
Machine Learning

Background:

Siamese tracking algorithms have advanced significantly, largely due to extensive training datasets.
The specific role of large datasets in enhancing siamese tracker effectiveness remains under-explored.

Purpose of the Study:

To analyze the impact of training data on siamese tracker learning from an optimization viewpoint.
To develop a novel data-free siamese tracking algorithm that leverages insights from data analysis.

Main Methods:

Investigated the function of training data in background suppression and target representation refinement.
Introduced SiamDF, a data-free algorithm requiring only a pre-trained backbone.
Enhanced background suppression by isolating target regions and employing inverse transformations for aspect ratio consistency.

Keywords:

Pre-training Scale estimation Sharing computation Siamese tracking Tracking training data-free

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Improved center displacement prediction by mitigating spatial stride deviations.

Main Results:

SiamDF demonstrates effective background suppression and refined target representation without additional training.
The algorithm maintains target state aspect ratio and improves center displacement prediction.
Experimental results show SiamDF achieves competitive performance against supervised and unsupervised methods on popular benchmarks.

Conclusions:

Large training datasets are crucial for background suppression in siamese tracking.
The proposed SiamDF algorithm offers a viable data-free approach to siamese tracking, eliminating the need for fine-tuning or online updates.
SiamDF achieves state-of-the-art performance, highlighting the potential of data-efficient learning strategies.