Target detection models are one of the widely used deep learning-based applications for reducing human efforts on video surveillance and patrol. However, the application of conventional computer vision-based target detection models in military usage can result in limited performance, due to the lack of sample data of hostile targets. In this paper, we present the possibility of the electroencephalography-based video target detection model, which could be applied as a supportive module of the military video surveillance system. The proposed framework and detection model showed prospective performance achieving a mean macro F-beta of 0.6522 with asynchronous real-time data from five subjects, in a certain video stimulus, but not on some video stimuli. By analyzing the results of experiments using each video stimulus, we present the factors that would affect the performance of electroencephalography-based video target detection models.

翻译：目标检测模型是广泛应用的基于深度学习的应用之一，旨在减少视频监控与巡逻中的人力投入。然而，当将传统计算机视觉目标检测模型应用于军事领域时，由于敌方目标样本数据的缺乏，其性能可能受限。本文提出了一种基于脑电图（EEG）的视频目标检测模型的可行性，该模型可作为军事视频监控系统的辅助模块。所提出的框架与检测模型在异步实时数据实验中，针对五名受试者在特定视频刺激下取得了具有前景的性能，平均宏F-beta值达到0.6522，但在某些视频刺激下效果不佳。通过分析各视频刺激的实验结果，我们揭示了影响基于脑电图的视频目标检测模型性能的因素。