A Bioinspired Retinal Neural Network for Accurately Extracting
  Small-Target Motion Information in Cluttered Backgrounds
  Robust and accurate detection of small moving targets in cluttered moving
backgrounds is a significant and challenging problem for robotic visual systems
to perform search and tracking tasks. Inspired by the neural circuitry of
elementary motion vision in the mammalian retina, this paper proposes a
bioinspired retinal neural network based on a new neurodynamics-based temporal
filtering and multiform 2-D spatial Gabor filtering. This model can estimate
motion direction accurately via only two perpendicular spatiotemporal filtering
signals, and respond to small targets of different sizes and velocities by
adjusting the dendrite field size of the spatial filter. Meanwhile, an
algorithm of directionally selective inhibition is proposed to suppress the
target-like features in the moving background, which can reduce the influence
of background motion effectively. Extensive synthetic and real-data experiments
show that the proposed model works stably for small targets of a wider size and
velocity range, and has better detection performance than other bioinspired
models. Additionally, it can also extract the information of motion direction
and motion energy accurately and rapidly.
