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Data for our paper "Deep Learning Based Virtual Point Tracking for Real-Time Target-less Dynamic Displacement Measurement in Railway Applications"
Shi, Dachuan
This is data used for training and evaluating the neural networks used in our paper.
The Abstract of our paper:
In the application of computer-vision-based displacement measurement, an optical target is usually required
to prove the reference. If the optical target cannot be attached to the measuring objective, edge detection,
feature matching, and template matching are the most common approaches in target-less photogrammetry.
However, their performance significantly relies on parameter settings. This becomes problematic in dynamic
scenes where complicated background texture exists and varies over time. We propose virtual point tracking
for real-time target-less dynamic displacement measurement, incorporating deep learning techniques and
domain knowledge to tackle this issue. Our approach consists of three steps: 1) automatic calibration for
detection of region of interest; 2) virtual point detection for each video frame using deep convolutional neural
network; 3) domain-knowledge based rule engine for point tracking in adjacent frames. The proposed
approach can be executed on an edge computer in a real-time manner (i.e. over 30 frames per second). We
demonstrate our approach for a railway application, where the lateral displacement of the wheel on the rail
is measured during operation. We also implemented an algorithm using template matching and line detection
as the baseline for comparison. The numerical experiments have been performed to evaluate our approach’s
performance and latency in a harsh railway environment with dynamic complex backgrounds. We make our
code and data available at https://github.com/quickhdsdc/Point-Tracking-for-Displacement-Measurement-
in-Railway-Applications.
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