Abstract：
Flying insects rely mainly upon visual motion to detect and track objects. There has been a lot of research on fly inspired algorithms for object detection, but few have been developed based on visual motion alone. One of the daunting difficulties is that the neural and circuit mechanisms underlying the foreground-background segmentation are still unclear. Our previous modeling study proposed that the lobula held parallel pathways with distinct directional selectivity, each of which could retinotopically discriminate figures moving in its own preferred direction based on relative motion cues. The previous model, however, did not address how the multiple parallel pathways gave the only detection output at their common downstream. Since the preferred directions of the pathways along either horizontal or vertical axis were opposite to each other, the background moving in the opposite direction to an object also activated the corresponding lobula pathway. Indiscriminate or ungated projection from all the pathways to their downstream would mix objects with the moving background, making the previous model fail with non-stationary background. Here, we extend the previous model by proposing that the background motion-dependent gating of individual lobula projections is the key to object detection. Large-field lobula plate tangential cells are hypothesized to perform the gating to realize bioinspired background subtraction. The model is shown to be capable of implementing a robust detection of moving objects in video sequences with either a moving camera that induces translational optic flow or a static camera. The model sheds light on the potential of the concise fly algorithm in real-world applications.
摘要：
飞行昆虫主要依靠视觉运动来检测和跟踪物体。已经有很多关于飞行启发的目标检测算法的研究，但是很少有仅仅基于视觉运动而开发的。令人生畏的困难之一是前景-背景分割的神经和电路机制仍不清楚。我们先前的建模研究提出，叶保持平行路径具有不同的方向选择性，每个人都可以根据相对运动线索，以视网膜方式区分沿自己的首选方向移动的数字。以前的模型，然而,没有解决多个并行路径如何在其共同的下游提供唯一的检测输出。由于路径沿水平轴或垂直轴的优选方向彼此相反，在与物体相反的方向上移动的背景也激活了相应的叶路径。从所有路径到下游的不分青红皂白或无门控投影会将物体与移动的背景混合在一起，使得以前的模型在非平稳背景下失败。这里,我们通过提出单个小叶投影的背景运动相关门控是目标检测的关键来扩展先前的模型。假设大视场小叶板切向细胞执行门控以实现生物启发背景减除。该模型显示能够使用引起平移光流的移动相机或静态相机来实现对视频序列中的移动对象的鲁棒检测。该模型揭示了简洁的苍蝇算法在实际应用中的潜力。
