Decoding the direction of translating objects in front of cluttered moving backgrounds, accurately and efficiently, is still a challenging problem. In nature, lightweight and low-powered flying insects apply motion vision to detect a moving target in highly variable environments during flight, which are excellent paradigms to learn motion perception strategies. This paper investigates the fruit fly Drosophila motion vision pathways and presents computational modelling based on cutting-edge physiological researches. The proposed visual system model features bio-plausible ON and OFF pathways, wide-field horizontal-sensitive (HS) and vertical-sensitive (VS) systems. The main contributions of this research are on two aspects: (1) the proposed model articulates the forming of both direction-selective and direction-opponent responses, revealed as principal features of motion perception neural circuits, in a feed-forward manner; (2) it also shows robust direction selectivity to translating objects in front of cluttered moving backgrounds, via the modelling of spatiotemporal dynamics including combination of motion pre-filtering mechanisms and ensembles of local correlators inside both the ON and OFF pathways, which works effectively to suppress irrelevant background motion or distractors, and to improve the dynamic response. Accordingly, the direction of translating objects is decoded as global responses of both the HS and VS systems with positive or negative output indicating preferred-direction or null-direction translation. The experiments have verified the effectiveness of the proposed neural system model, and demonstrated its responsive preference to faster-moving, higher-contrast and larger-size targets embedded in cluttered moving backgrounds.

在杂乱的移动背景前，准确高效地解码平移对象的方向仍然是一个具有挑战性的问题。在自然界中，轻型和低功率飞行昆虫在飞行过程中应用运动视觉来检测高度可变环境中的运动目标，这是学习运动感知策略的绝佳范例。本文研究了果蝇果蝇运动视觉路径，并提供基于尖端生理研究的计算模型。所提出的视觉系统模型具有生物合理的开和关路径、宽视场水平敏感 (HS) 和垂直敏感 (VS) 系统。这项研究的主要贡献有两个方面：（1）所提出的模型以前馈方式阐明了方向选择性和方向相反反应的形成，作为运动感知神经回路的主要特征；(2) 通过时空动力学建模，包括运动预过滤机制的组合和 ON 和 OFF 路径内局部相关器的集合，它还显示了在杂乱的移动背景前平移对象的稳健方向选择性，它可以有效地抑制不相关的背景运动或干扰因素，并改善动态响应。因此，翻译对象的方向被解码为 HS 和 VS 系统的全局响应，具有指示首选方向或空方向翻译的正或负输出。实验验证了所提出的神经系统模型的有效性，并证明了其对嵌入在杂乱移动背景中的更快移动、更高对比度和更大尺寸目标的响应偏好。