Mimicking fly motion tracking and fixation behaviors with a hybrid visual neural network

How do animals like insects perceive meaningful visual motion cues involving directional and locational information of moving objects in visual clutter accurately and efficiently? In this paper, with respect to latest biological research progress made in underlying motion detection circuitry in the fly's preliminary visual system, we conduct a novel hybrid visual neural network, combining the functionality of two bio-plausible, namely the motion and the position pathways, for mimicking motion tracking and fixation behaviors. This modeling study extends a former direction selective neurons model to the higher level of behavior. The motivated algorithms can be used to guide a system that extracts location information of moving objects in a scene regardless of background clutter, using entirely low-level visual processing. We tested it against translational movements in synthetic and real-world scenes. The results demonstrated the following contributions: (1) The proposed computational structure fulfills the characteristics of a putative signal tuning map of the fly's physiology. (2) It also satisfies a biological implication that visual fixation behaviors could be simply tuned via the position pathway; nevertheless, the motion-detecting pathway improves the tracking precision. (3) Contrary to segmentation and registration based computer vision techniques, its computational simplicity benefits the building of neuromorphic visual sensor for robots.