Abstract: Crowd escape behavior in public places is easy to cause serious public safety disasters. Traditional computer vision technology can detect a few characteristics of crowd escape behavior, but it is difficult to face complex dynamic visual scenes. To address this issue, based on the structure characteristics of locust visual nerve, the danger perception mechanism of locust Lobula Giant Movement Detector (LGMD) and mammalian retinal luminance adaptation mechanism, this paper proposes an Enhanced Crowd Escape Detection Neural Network (ECEDNN). The proposed neural network collects the luminance changes caused by crowd activities in the field of view. With the help of the mammalian retinal luminance adaptive mechanism, the visual response excitation is tuned to adapt to the lighting scene. Visual excitation and suppression are mixed to filter background noise and center-surround mechanism was used to enhance motion edges. Finally, neural spike adaptive tuning is used to detect the burst escape behavior of the crowd and output strong membrane potential excitation. This work is involved the research of crowd activity detection inspired by biological visual perception mechanism, which can provide new ideas and methods for crowd behavior activity perception and anomaly detection in artificial intelligence.

摘要： 公共场所人群逃逸行为极易引发严重的公共安全灾难事故，传统计算机视觉技术能检测其少许特征，但面对复杂动态视觉场景捉襟见肘。针对该问题，基于蝗虫视觉神经结构特性、借助蝗虫小叶巨型运动检测器（LGMD）危险感知机理、哺乳动物视网膜流明自适应机制，提出一种增强型人群逃逸检测神经网络（ECEDNN）。所提出的神经网络采集视野域中人群活动引发的流明变化；借助哺乳动物视网膜流明自适应机制，调谐视觉响应兴奋以适应光照场景；视觉兴奋与抑制混合过滤背景噪声并采用中心环绕机制增强运动边缘；最后，神经尖峰自适应调谐用于实现对人群突发逃逸行为的检测并对其输出强烈膜电位兴奋。论文工作涉及生物视感机制启发的人群活动检测研究，可为人工智能中的人群行为活动感知、异常检测等提供新思想、新方法。