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：目标检测模型是基于深度学习的广泛应用之一，旨在减少视频监控和巡逻中的人力投入。然而，在军事用途中，由于缺乏敌对目标的样本数据，传统基于计算机视觉的目标检测模型性能可能受限。本文提出了一种基于脑电图（EEG）的视频目标检测模型的可能性，该模型可作为军事视频监控系统的支持模块。所提出的框架和检测模型在异步实时数据下展现出潜力，五名受试者在特定视频刺激下平均宏$F_{\beta}$值达到0.6522，但在某些视频刺激下效果不佳。通过分析每个视频刺激的实验结果，我们总结了影响基于脑电的视频目标检测模型性能的因素。