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Target detection models are one of the widely used deep learning-basedapplications for reducing human efforts on video surveillance and patrol.However, the application of conventional computer vision-based target detectionmodels in military usage can result in limited performance, due to the lack ofsample data of hostile targets. In this paper, we present the possibility ofthe electroencephalography-based video target detection model, which could beapplied as a supportive module of the military video surveillance system. Theproposed framework and detection model showed prospective performance achievinga mean macro F-beta of 0.6522 with asynchronous real-time data from fivesubjects, in a certain video stimulus, but not on some video stimuli. Byanalyzing the results of experiments using each video stimulus, we present thefactors that would affect the performance of electroencephalography-based videotarget detection models.

Influence of Video Dynamics on EEG-based Single-Trial Video Target Surveillance Syste