  As deep learning continues to dominate all state-of-the-art computer vision
tasks, it is increasingly becoming the essential building blocks for robotic
perception. As a result, the research questions concerning the safety and
reliability of learning-based perception are gaining increased importance.
Although there is an established field that studies safety certification and
convergence guarantee of complex software systems for decision-making during
design-time, the uncertainty in run-time conditions and the unknown future
deployment environments of autonomous systems as well as the complexity of
learning-based perception systems make the generalisation of the verification
results from design-time to run-time problematic. More attention is starting to
shift towards run-time monitoring of performance and reliability of perception
systems with several trends emerging in the literature in the face of such a
challenge. This paper attempts to identify these trends and summarise the
various approaches on the topic.
