  The robust detection of small targets against cluttered background is
important for future artificial visual systems in searching and tracking
applications. The insects' visual systems have demonstrated excellent ability
to avoid predators, find prey or identify conspecifics - which always appear as
small dim speckles in the visual field. Build a computational model of the
insects' visual pathways could provide effective solutions to detect small
moving targets. Although a few visual system models have been proposed, they
only make use of small-field visual features for motion detection and their
detection results often contain a number of false positives. To address this
issue, we develop a new visual system model for small target motion detection
against cluttered moving backgrounds. Compared to the existing models, the
small-field and wide-field visual features are separately extracted by two
motion-sensitive neurons to detect small target motion and background motion.
These two types of motion information are further integrated to filter out
false positives. Extensive experiments showed that the proposed model can
outperform the existing models in terms of detection rates.
