A Directionally Selective Small Target Motion Detecting Visual Neural Network in Cluttered Backgrounds

Abstract

Discriminating targets moving against a cluttered background is a huge challenge, let alone detecting a target as small as one or a few pixels and tracking it in flight. In the insect's visual system, a class of specific neurons, called small target motion detectors (STMDs), have been identified as showing exquisite selectivity for small target motion. Some of the STMDs have also demonstrated direction selectivity which means these STMDs respond strongly only to their preferred motion direction. Direction selectivity is an important property of these STMD neurons which could contribute to tracking small targets such as mates in flight. However, little has been done on systematically modeling these directionally selective STMD neurons. In this paper, we propose a directionally selective STMD-based neural network for small target detection in a cluttered background. In the proposed neural network, a new correlation mechanism is introduced for direction selectivity via correlating signals relayed from two pixels. Then, a lateral inhibition mechanism is implemented on the spatial field for size selectivity of the STMD neurons. Finally, a population vector algorithm is used to encode motion direction of small targets. Extensive experiments showed that the proposed neural network not only is in accord with current biological findings, i.e., showing directional preferences, but also worked reliably in detecting small targets against cluttered backgrounds.

Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

“…Wiederman et al have proposed that the lateral inhibition mechanism plays a crucial role to adjust the size selectivity via spatiotemporal neural computation [229,230]. Derived from this theory, Wang et al mathematically analysed the way of generating the size selectivity in motion sensitive visual pathways of insects: in this research, they applied a second-order lateral inhibition mechanism in the computational layer of Lobula which can be represented by an algorithm of 'Difference of Gaussians' [222].…”

A Robust Visual System for Small Target Motion Detection Against Cluttered Moving Backgrounds

“…Multiple neural circuits could be coordinated to discriminate small target motion. For example, in the lamina layer, large monopolar cells (LMCs) [14], [15] have been described as temporal band-pass filters which extract motion information from luminance signals [12], [13], [19]; and amacrine cells (AMCs) [20]- [22] linked to multi adjacent ommatidia with thin extending fibers, may constitute a contrast pathway with their downstream neurons to extract directional contrast from luminance signals. Although the contribution from the AMCs to STMD neural circuits in insects is unknown, it is clear that with directional contrast and motion information together, an artificial vision system could discriminate small moving targets from fake features robustly.…”

“…It can detect the presence of small moving targets, but is unable to estimate motion direction. To address this issue, directional selectivity has been introduced into the ESTMD [13], [19], [25]. However, these models cannot discriminate small targets from fake features, as they only make use of motion information.…”

“…When a dim object successively moves over pixels (x, y) and (x (θ), y (θ)), a luminance decrease followed by a luminance increase will appear at each of these two pixels. These luminance increase and decrease signals are first aligned in time domain and then multiplied together so as to produce a large response [13]. That is,…”

“…5). It is worthy to note that τ 3 , τ 4 and τ 5 are determined by the different delays among the luminance changes, while n 3 , n 4 and n 5 are accordingly tuned to guarantee appropriate Gamma kernel shapes [13].…”