TY - GEN
T1 - Tunnel moving target detection based on local structure of image and gray scale information
AU - Yu, Haiyang
AU - Hu, Yawen
AU - Guo, Hongyu
AU - Fang, Lin
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/3
Y1 - 2016/10/3
N2 - Tunnel moving target detection is subject to the influence of light condition and motion blur, the traditional motion detection method based on pixel points can not be very good at segmenting moving target. To solve this problem, frame difference detection method based on local structure of image and gray level information is proposed. The local mean difference information of the image is calculated by the improved algorithm, and then the similarity measure function and the gray scale measure function are constructed. The similarity measure function effectively describes the structural features of moving objects, and reduces the influence of image background information. The gray scale function is better to highlight the contrast of the target brightness, to increase the division of the target area and the background parts, and to realize the moving target detection correctly. The experimental results show that the detection method of the fusion structure and the gray level information can effectively segment the moving object.
AB - Tunnel moving target detection is subject to the influence of light condition and motion blur, the traditional motion detection method based on pixel points can not be very good at segmenting moving target. To solve this problem, frame difference detection method based on local structure of image and gray level information is proposed. The local mean difference information of the image is calculated by the improved algorithm, and then the similarity measure function and the gray scale measure function are constructed. The similarity measure function effectively describes the structural features of moving objects, and reduces the influence of image background information. The gray scale function is better to highlight the contrast of the target brightness, to increase the division of the target area and the background parts, and to realize the moving target detection correctly. The experimental results show that the detection method of the fusion structure and the gray level information can effectively segment the moving object.
KW - frame difference detection
KW - gray scale measure function
KW - local structure of image
KW - moving target detection
KW - similarity measure function
UR - https://www.scopus.com/pages/publications/84994491808
U2 - 10.1109/ICITE.2016.7581316
DO - 10.1109/ICITE.2016.7581316
M3 - 会议稿件
AN - SCOPUS:84994491808
T3 - 2016 IEEE International Conference on Intelligent Transportation Engineering, ICITE 2016
SP - 103
EP - 107
BT - 2016 IEEE International Conference on Intelligent Transportation Engineering, ICITE 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Intelligent Transportation Engineering, ICITE 2016
Y2 - 20 August 2016 through 22 August 2016
ER -