TY - GEN
T1 - Brain-computer Fusion Low-quality Video Target Detection Method
AU - Shi, Jianting
AU - Bi, Luzheng
AU - Xu, Xinbo
AU - Fei, Weijie
AU - Feleke, Aberham Genetu
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Video target detection technology has made significant progress in recent years. However, in the face of low-quality video targets, traditional computer vision methods are limited by unclear features or missing information. In this paper, we propose an innovative video target detection method that combines computer vision techniques with non-invasive brain-computer interface (BCI) techniques, aiming to address the low robustness of computer vision with the high false alarm rate of BCI methods. This method combines the advantages of the two systems, combining the rapidity of computer vision technology and the high robustness and anti-interference of BCI technology. Experimental results show that the proposed method improves the detection performance of low-quality video target detection method, and achieves performance improvement compared with the single method. This paper brings new technical ideas to the field of video target detection, and also provides new possibilities for the expansion of BCI technology in practical applications.
AB - Video target detection technology has made significant progress in recent years. However, in the face of low-quality video targets, traditional computer vision methods are limited by unclear features or missing information. In this paper, we propose an innovative video target detection method that combines computer vision techniques with non-invasive brain-computer interface (BCI) techniques, aiming to address the low robustness of computer vision with the high false alarm rate of BCI methods. This method combines the advantages of the two systems, combining the rapidity of computer vision technology and the high robustness and anti-interference of BCI technology. Experimental results show that the proposed method improves the detection performance of low-quality video target detection method, and achieves performance improvement compared with the single method. This paper brings new technical ideas to the field of video target detection, and also provides new possibilities for the expansion of BCI technology in practical applications.
KW - Brain-computer fusion
KW - Brain-computer interface
KW - EEG signals
KW - Video-target detection
UR - http://www.scopus.com/inward/record.url?scp=85218024238&partnerID=8YFLogxK
U2 - 10.1109/ICUS61736.2024.10840016
DO - 10.1109/ICUS61736.2024.10840016
M3 - Conference contribution
AN - SCOPUS:85218024238
T3 - Proceedings of 2024 IEEE International Conference on Unmanned Systems, ICUS 2024
SP - 1055
EP - 1059
BT - Proceedings of 2024 IEEE International Conference on Unmanned Systems, ICUS 2024
A2 - Song, Rong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Unmanned Systems, ICUS 2024
Y2 - 18 October 2024 through 20 October 2024
ER -