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Attention-Driven Memory Network for Online Visual Tracking.

Huanlong Zhang, Jiamei Liang, Jiapeng Zhang

IEEE Transactions on Neural Networks and Learning Systems

|August 11, 2023

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This study introduces an attention-driven memory network for online object tracking, enhancing robustness by integrating short-term and long-term memory modules. The method effectively mines discriminative target information for improved tracking accuracy in complex scenes.

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Object tracking in complex scenes is challenging due to difficulties in retaining intrinsic target attributes.
Existing memory mechanisms struggle to provide essential target information to trackers.
Biological visual memory offers inspiration for improving tracking robustness and reliability.

Purpose of the Study:

To propose a novel online tracking method using an attention-driven memory network.
To enhance tracker robustness and reliability by mining discriminative memory information.
To leverage biological memory mechanisms for improved object tracking.

Main Methods:

Designed a novel attention-driven memory network with long and short-term memory modules.

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Published on: January 18, 2020

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Published on: November 14, 2018

The long memory module captures property-level information at channel and spatial levels.

An online memory updater (MU) refines memory content based on tracking confidence and uses Mixer layer and online head network.

Main Results:

The attention-driven memory network adaptively balances short-term and long-term memory contributions.
The online memory updater ensures effective model updates by evaluating tracking confidence.
The proposed method demonstrates favorable performance across multiple benchmark datasets (OTB, TC-128, UAV-123, GOT-10k, VOT-2016, VOT-2018).

Conclusions:

The proposed attention-driven memory network effectively mines discriminative information for robust online tracking.
The integration of biological memory principles enhances tracking performance in challenging scenarios.
The method achieves state-of-the-art results, validating its effectiveness on diverse datasets.