A Moving Target Detection Model Inspired by Spatio-Temporal Information Accumulation of Avian Tectal Neurons

Abstract

Moving target detection in cluttered backgrounds is always considered a challenging problem for artificial visual systems, but it is an innate instinct of many animal species, especially the avian. It has been reported that spatio-temporal information accumulation computation may contribute to the high efficiency and sensitivity of avian tectal neurons in detecting moving targets. However, its functional roles for moving target detection are not clear. Here we established a novel computational model for detecting moving targets. The proposed model mainly consists of three layers: retina layer, superficial layers of optic tectum, and intermediate-deep layers of optic tectum; in the last of which motion information would be enhanced by the accumulation process. The validity and reliability of this model were tested on synthetic videos and natural scenes. Compared to EMD, without the process of information accumulation, this model satisfactorily reproduces the characteristics of tectal response. Furthermore, experimental results showed the proposed model has significant improvements over existing models (EMD, DSTMD, and STMD plus) on STNS and RIST datasets. These findings do not only contribute to the understanding of the complicated processing of visual motion in avians, but also further provide a potential solution for detecting moving targets against cluttered environments.