TY - GEN
T1 - Improved Mixed Gaussian Model for Background Subtraction Based on Color Channel Fusion
AU - Zhuo, Yang
AU - Deqiang, Han
AU - Xu, Zhanbo
AU - Yu, Yang
N1 - Publisher Copyright:
© 2023 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2023
Y1 - 2023
N2 - Motion object detection, which has a wide range of applications in video surveillance systems, extracts motion objects from the video. Gaussian Mixture Model (GMM) has achieved widespread success in motion object detection because of its good performance. However, it simplifies color channel information or directly uses grayscale information. If the multi-color channel information can be jointly used, it is expected to obtain better results in complex scenes. Therefore, we design the multi-color channel voting GMM by jointly using the multi-color channel information and introduce soft voting based on soft decision to further strengthen the use of information. Experimental results show that the multi-color channel voting GMM proposed in this paper can well detect motion objects in complex scenes. Compared with original GMM algorithms, multi-color channel voting GMM has a better F-measure metric in the complex scenarios.
AB - Motion object detection, which has a wide range of applications in video surveillance systems, extracts motion objects from the video. Gaussian Mixture Model (GMM) has achieved widespread success in motion object detection because of its good performance. However, it simplifies color channel information or directly uses grayscale information. If the multi-color channel information can be jointly used, it is expected to obtain better results in complex scenes. Therefore, we design the multi-color channel voting GMM by jointly using the multi-color channel information and introduce soft voting based on soft decision to further strengthen the use of information. Experimental results show that the multi-color channel voting GMM proposed in this paper can well detect motion objects in complex scenes. Compared with original GMM algorithms, multi-color channel voting GMM has a better F-measure metric in the complex scenarios.
KW - Gaussian mixture model
KW - Object detection
KW - Soft voting fusion
UR - https://www.scopus.com/pages/publications/85175548442
U2 - 10.23919/CCC58697.2023.10240731
DO - 10.23919/CCC58697.2023.10240731
M3 - 会议稿件
AN - SCOPUS:85175548442
T3 - Chinese Control Conference, CCC
SP - 7965
EP - 7970
BT - 2023 42nd Chinese Control Conference, CCC 2023
PB - IEEE Computer Society
T2 - 42nd Chinese Control Conference, CCC 2023
Y2 - 24 July 2023 through 26 July 2023
ER -