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Image-based Lagrangian Particle Tracking in Bed-load Experiments

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Three-stage cascade architecture-based siamese sliding window network algorithm for object tracking.

Zheng Yang¹, Kaiwen Liu², Quanlong Li²

¹School of Electrical Engineering, Yellow River Conservancy Technical Institute, Dongjing street, Kaifeng, 475004, Henan, China.

|February 3, 2025

Summary

This summary is machine-generated.

The Siam ST algorithm uses a three-stage cascade architecture to improve object tracking by capturing global image information and enhancing feature correlation. This robust method significantly outperforms existing algorithms on multiple benchmark datasets.

Keywords:

Deep learning Siamese networks Single object tracking Sliding window Three-level cascade

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Object tracking is a critical task in computer vision, requiring robust feature extraction and correlation.
Existing methods often struggle with complex scenarios, leading to performance degradation.

Purpose of the Study:

To propose the Siam ST algorithm for enhanced object tracking.
To improve the correlation of feature information and enrich cross-correlation metrics.

Main Methods:

Implemented a three-stage cascade (TSC) architecture for object tracking.
Introduced a sliding window in the last three convolution layers to capture global image information.
Utilized a regional proposal network within the TSC structure for inter-frame feature interaction.

Main Results:

The Siam ST algorithm demonstrated high robustness and effective association feature extraction.
Ablation studies on VOT2016 and comparative experiments on VOT2018, LaSOT, Tracking Net, and UAV123 were conducted.
The proposed algorithm showed significant improvements over SiamRPN++ across all tested datasets.

Conclusions:

The Siam ST algorithm, with its novel TSC architecture and sliding window approach, offers superior performance in object tracking.
The method effectively enhances feature correlation and robustness, outperforming state-of-the-art trackers.