  As deep learning continues to dominate all state-of-the-art computer vision
tasks, it is increasingly becoming an essential building block for robotic
perception. This raises important questions concerning the safety and
reliability of learning-based perception systems. There is an established field
that studies safety certification and convergence guarantees of complex
software systems at design-time. However, the unknown future deployment
environments of an autonomous system and the complexity of learning-based
perception make the generalization of design-time verification to run-time
problematic. In the face of this challenge, more attention is starting to focus
on run-time monitoring of performance and reliability of perception systems
with several trends emerging in the literature. This paper attempts to identify
these trends and summarise the various approaches to the topic.
