Multilayer deep feature integration framework for discriminative correlation filter tracking

You Zhai; Xiwei Guo; Peng He

doi:10.1117/12.3022758

28 March 2024 Multilayer deep feature integration framework for discriminative correlation filter tracking

You Zhai, Xiwei Guo, Peng He

Proceedings Volume 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023); 130910I (2024) https://doi.org/10.1117/12.3022758
Event: Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 2023, Xi’an, China

Abstract

Due to the complexity of deep features, discriminative correlation filter (DCF)-based trackers only extract single-layer deep features for object description and the characteristic of different layers cannot be fully exploited. Therefore, a framework of integrating multi-layer deep features is proposed for trackers based on DCF. In the training stage, multi-layer deep features are extracted using convolutional neural network. Max pooling and channel compression is applied to deep features to reduce feature dimensions and channels. Then the compressed deep features are cascaded with hand crafted features for correlation filter training. In the tracking stage, only hand-crafted features are extracted in multi-scales and deep features are extracted in single scale. Then tracking responses from single scale deep features are added to responses from multi-scale hand crafted features. Experiment results show that the tracking precision and success rate is 3.7% and 3.9% higher than original LADCF respectively. Meanwhile, the speed is 48.6% faster. It indicates that the proposed method is feasible which can maintain the performance of DCF trackers and improve the speed of the algorithm.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

You Zhai, Xiwei Guo, and Peng He "Multilayer deep feature integration framework for discriminative correlation filter tracking", Proc. SPIE 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 130910I (28 March 2024); https://doi.org/10.1117/12.3022758

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Detection and tracking algorithms

Feature extraction

Tunable filters

Image filtering

Histograms of oriented gradient

Image resolution

Convolutional neural networks

Show All Keywords

Keywords/Phrases

Search In:

Publication Years