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SNS-CF: Siamese Network with Spatially Semantic Correlation Features for Object Tracking.

Thierry Ntwari¹, Hasil Park¹, Joongchol Shin¹

¹Graduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University, Seoul 06974, Korea.

Sensors (Basel, Switzerland)

|September 3, 2020

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This study introduces Siamese Network with Spatially Semantic Correlation Features (SNS-CF) for robust object tracking. SNS-CF combines deep Siamese networks and correlation filters to achieve high accuracy and real-time performance.

Area of Science:

Computer Vision
Machine Learning

Background:

Deep Siamese networks have advanced object tracking but lack the accuracy of correlation filters.
Correlation filters offer high accuracy but struggle with frame updates.

Discussion:

The proposed Siamese Network with Spatially Semantic Correlation Features (SNS-CF) integrates Siamese networks and correlation filters.
SNS-CF employs a Siamese feature extractor, a spatially semantic feature extractor, and an adaptive correlation filter.
This fusion addresses limitations of individual methods for enhanced object tracking.

Key Insights:

SNS-CF achieves accurate and robust object tracking by combining deep features and correlation filtering.
The spatially semantic feature extractor effectively handles diverse features across image regions.

Keywords:

correlation filter object tracking siamese network spatially semantic correlation features

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The adaptive correlation filter ensures efficient frame updates and high tracking accuracy.

Outlook:

SNS-CF demonstrates favorable performance compared to state-of-the-art methods on multiple benchmarks.
This approach offers a promising direction for high frame rate, real-time visual tracking systems.