Motion target detection suffers from low accuracy,poor integrity,and is prone to ghosting interference.In order to solve these problems and improve the background fitness of moving target detection,this paper combines the adaptive VIBE algorithm with HOG matching ghost,and proposes a moving target correction algorithm,namely the HAVB algorithm.Firstly,the ADVIBE algorithm is used to separate the target background from the preprocessed im-age,and the background dynamic adaptability is improved by automatically updating the matching and distance thresholds;then,the HOG algorithm is used to calculate the matching and set the value to zero,so as to detect and suppress the ghost influence and improve the integrity of target detection.Then the shadow region is detected and e-liminated by extracting the shadow probability distribution point by point matching algorithm,and the error noise is suppressed based on morphological operation and threshold noise reduction to improve the accuracy of target detection.The experimental results of different image correction baseline algorithms show that,on the AEID aerobics image data set,compared with the traditional algorithm,the proposed HAVB algorithm has high accuracy,strong sta-bility and high background dynamic adaptability:the P,R and F index parameters are increased by 6.6%,6.1% and 6.2% on average.The background extraction time is reduced by 47.9% on average.In summary,the HAVB image ghost correction algorithm model constructed in this paper effectively improves the integrity and accuracy of target de-tection,and lays a good foundation for target recognition and tracking,which has important research significance.
维普
万方数据