  In the application of computer-vision based displacement measurement, an
optical target is usually required to prove the reference. In the case that 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. To tackle this
issue, we propose virtual point tracking for real-time target-less dynamic
displacement measurement, incorporating deep learning techniques and domain
knowledge. 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 implement an algorithm using template matching and line detection as the
baseline for comparison. The numerical experiments have been performed to
evaluate the performance and the latency of our approach in the harsh railway
environment with noisy and varying backgrounds.
