  Visual dynamic complexity is a ubiquitous, hidden attribute of the visual
world that every dynamic vision system is faced with. However, it is implicit
and intractable which has never been quantitatively described due to the
difficulty in defending temporal features correlated to spatial image
complexity. To fill this vacancy, we propose a novel bio-robotic approach to
profile visual dynamic complexity which can be used as a new metric. Here we
apply a state-of-the-art brain-inspired motion detection neural network model
to explicitly profile such complexity associated with spatial-temporal
frequency (SF-TF) of visual scene. This model is for the first time implemented
in an autonomous micro-mobile robot which navigates freely in an arena with
visual walls displaying moving sine-wave grating or cluttered natural scene.
The neural dynamic response can make reasonable prediction on surrounding
complexity since it can be mapped monotonically to varying SF-TF of visual
scene. The experiments show this approach is flexible to different visual
scenes for profiling the dynamic complexity. We also use this metric as a
predictor to investigate the boundary of another collision detection visual
system performing in changing environment with increasing dynamic complexity.
This research demonstrates a new paradigm of using biologically plausible
visual processing scheme to estimate dynamic complexity of visual scene from
both spatial and temporal perspectives, which could be beneficial to predicting
input complexity when evaluating dynamic vision systems.
