Efficient Bio-Robotic Estimation of Visual Dynamic Complexity

Visual dynamic complexity is ubiquitous, hidden attribute of the visual world that every motion-sensitive vision system is faced with. However, it is implicit and intractable which has never been quantitatively described due to difficulty in defending temporal features correlated to spatial image complexity. Learning from biological visual processing, we propose a novel bio-robotic approach to estimate visual dynamic complexity, effectively and efficiently, which can be used as a new metric for assessing dynamic vision systems implemented in robots. Here we apply a bio-inspired neural network model to quantitatively estimate such complexity associated with spatial-temporal frequency of moving visual scene. The model is implemented in an autonomous micro-mobile robot navigating freely in an arena encompassed by visual walls displaying moving scenes. The response of the embedded visual module can make reasonable prediction on surrounding dynamic complexity since it can be mapped monotonically to varying moving frequencies of visual scene. The experiments demonstrate this “predictor” is effective against different visual scenarios that can be established as a new metric for assessing visual systems. To prove its viability, we utilise it to investigate the performance boundary of a collision detection visual system in changing environment with increasing dynamic complexity.