Biologically Inspired Neural Networks for Crowd Escape Detection in Complex Scenes

Crowd escape behavior in public places is highly likely 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 structural characteristics of the locust visual nerve, and leveraging the danger perception mechanism of the locust Lobula Giant Movement Detector (LGMD) as well as the mammalian retinal luminance adaptation mechanism, this paper proposes an Enhanced Crowd Escape Detection Neural Network (ECEDNN). First, 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 combined to filter background noise, and a center-surround mechanism is 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. The experimental results show that ECEDNN can effectively detect and warn of crowd escape behavior in complex scenes, with an average accuracy of 98.90% on multiple video datasets. 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.