  This paper provides the first real-world evaluation of using visual (RGB
camera) data and machine learning for proactively predicting millimeter wave
(mmWave) dynamic link blockages before they happen. Proactively predicting
line-of-sight (LOS) link blockages enables mmWave/sub-THz networks to make
proactive network management decisions, such as proactive beam switching and
hand-off) before a link failure happens. This can significantly enhance the
network reliability and latency while efficiently utilizing the wireless
resources. To evaluate this gain in reality, this paper (i) develops a computer
vision based solution that processes the visual data captured by a camera
installed at the infrastructure node and (ii) studies the feasibility of the
proposed solution based on the large-scale real-world dataset, DeepSense 6G,
that comprises multi-modal sensing and communication data. Based on the adopted
real-world dataset, the developed solution achieves $\approx 90\%$ accuracy in
predicting blockages happening within the future $0.1$s and $\approx 80\%$ for
blockages happening within $1$s, which highlights a promising solution for
mmWave/sub-THz communication networks.
