Inspired by the motion processing pathway, this paper proposes a bio-inspired feedforward spiking network model based on Hodgkin–Huxley neurons for human motion perception. The proposed network mimics the mechanisms of direction selectivity found in simple and complex cells of the primary visual cortex. Simple cells' receptive fields are modeled using Gabor energy filters, while complex cells' receptive fields are constructed by integrating the responses of simple cells in an energy model. To generate the motion map, the spiking output of the network integrates motion information encoded by the responses of complex cells with various preferred directions. Simulation results demonstrate that the spiking neuron-based network effectively replicates the directional selectivity operation of the visual cortex when presented with a sequence of time-varying images. We evaluate the proposed model against state-of-the-art spiking neuron-based motion detection models using publicly available datasets. The results highlight the model's capability to extract motion energy from diverse video sequences, akin to human visual motion perception models. Additionally, we showcase the application of the proposed model in motion segmentation tasks and compare its performance with state-of-the-art motion-based segmentation models using challenging video segmentation benchmarks. The results indicate competitive performance. The motion maps generated by the proposed model can be utilized for action recognition in input videos.

受运动处理路径的启发，本文提出了一种基于霍奇金-赫胥黎神经元的生物启发前馈尖峰网络模型，用于人类运动感知。所提出的网络模仿了初级视觉皮层的简单和复杂细胞中发现的方向选择性机制。简单细胞的感受野是使用 Gabor 能量滤波器建模的，而复杂细胞的感受野是通过将简单细胞的响应集成到能量模型中来构建的。为了生成运动图，网络的尖峰输出集成了由具有各种首选方向的复杂细胞的响应编码的运动信息。仿真结果表明，当呈现一系列时变图像时，基于尖峰神经元的网络有效地复制了视觉皮层的方向选择性操作。我们使用公开可用的数据集，针对最先进的基于尖峰神经元的运动检测模型来评估所提出的模型。结果凸显了该模型从不同视频序列中提取运动能量的能力，类似于人类视觉运动感知模型。此外，我们展示了所提出的模型在运动分割任务中的应用，并使用具有挑战性的视频分割基准将其性能与最先进的基于运动的分割模型进行比较。结果表明有竞争力的表现。由所提出的模型生成的运动图可用于输入视频中的动作识别。