Video surveillance system is used in various fields such as transportation and social life.The bad weather can lead to the degradation of the video surveillance image quality.In rainy environment,the raindrops and the...Video surveillance system is used in various fields such as transportation and social life.The bad weather can lead to the degradation of the video surveillance image quality.In rainy environment,the raindrops and the background are mixed,which lead to make the image degradation,so the removal of the raindrops has great significance for image restoration.In this article,after analyzing the inter-frame difference method in detecting and removing raindrops,a background difference method is proposed based on Gaussian model.In this method,the raindrop is regarded as a moving object relative to the background.The principle and procedure of the method are given to detect and remove raindrops.The parameters of the single Gaussian background model are studied in this article.The important parameter of the learning rate of Gaussian model is explored in order to better detection and removal of raindrops.Experiment shows that the results of removal of raindrops by using the proposed algorithm are better than that by using the inter-frame difference method.The image processing effect is the best when the learning rate is 0.6.The research results can provide technical reference for similar research on eliminating the influence of rainy weather.展开更多
A dynamic learning rate Gaussian mixture model（GMM）algorithm is proposed to deal with the problem of slow adaption of GMM in the case of moving object detection in the outdoor surveillance,especially in the presence...A dynamic learning rate Gaussian mixture model（GMM）algorithm is proposed to deal with the problem of slow adaption of GMM in the case of moving object detection in the outdoor surveillance,especially in the presence of sudden illumination changes.The GMM is mostly used for detecting objects in complex scenes for intelligent monitoring systems.To solve this problem,a mixture Gaussian model has been built for each pixel in the video frame,and according to the scene change from the frame difference,the learning rate of GMM can be dynamically adjusted.The experiments show that the proposed method gives good results with an adaptive GMM learning rate when we compare it with GMM method with a fixed learning rate.The method was tested on a certain dataset,and tests in the case of sudden natural light changes show that our method has a better accuracy and lower false alarm rate.展开更多
Aiming at the fixed-view video surveillance scene,this paper proposes a video object detection method that combines motion features and YOLO.The method uses the method of filtering video frames without motion features...Aiming at the fixed-view video surveillance scene,this paper proposes a video object detection method that combines motion features and YOLO.The method uses the method of filtering video frames without motion features and segmenting video frames with motion features to reduce the reasoning pressure of the YOLO algorithm model.In this process,video frames containing moving objects are first obtained by the moving object detection module.Second,the moving target will be recognized by the object of interest recognition module.Finally,the background decision module records and analyzes the detection results to obtain background model updates or result output.It detects moving objects without using traditional background modeling methods.Experiments based on theCDnet2014 dataset showthat our method improves the missed detection rate by 0.098% and the average inference speed per frame by 45.62%compared with the YOLO-based humanoid detection method.Furthermore,the method has superior performance in scenarios where target objects appear less frequently(substations,transmission lines,and hazardous areas).展开更多
基金This work was supported by Henan Province Science and Technology Project under Grant No.182102210065.
文摘Video surveillance system is used in various fields such as transportation and social life.The bad weather can lead to the degradation of the video surveillance image quality.In rainy environment,the raindrops and the background are mixed,which lead to make the image degradation,so the removal of the raindrops has great significance for image restoration.In this article,after analyzing the inter-frame difference method in detecting and removing raindrops,a background difference method is proposed based on Gaussian model.In this method,the raindrop is regarded as a moving object relative to the background.The principle and procedure of the method are given to detect and remove raindrops.The parameters of the single Gaussian background model are studied in this article.The important parameter of the learning rate of Gaussian model is explored in order to better detection and removal of raindrops.Experiment shows that the results of removal of raindrops by using the proposed algorithm are better than that by using the inter-frame difference method.The image processing effect is the best when the learning rate is 0.6.The research results can provide technical reference for similar research on eliminating the influence of rainy weather.
文摘A dynamic learning rate Gaussian mixture model（GMM）algorithm is proposed to deal with the problem of slow adaption of GMM in the case of moving object detection in the outdoor surveillance,especially in the presence of sudden illumination changes.The GMM is mostly used for detecting objects in complex scenes for intelligent monitoring systems.To solve this problem,a mixture Gaussian model has been built for each pixel in the video frame,and according to the scene change from the frame difference,the learning rate of GMM can be dynamically adjusted.The experiments show that the proposed method gives good results with an adaptive GMM learning rate when we compare it with GMM method with a fixed learning rate.The method was tested on a certain dataset,and tests in the case of sudden natural light changes show that our method has a better accuracy and lower false alarm rate.
文摘Aiming at the fixed-view video surveillance scene,this paper proposes a video object detection method that combines motion features and YOLO.The method uses the method of filtering video frames without motion features and segmenting video frames with motion features to reduce the reasoning pressure of the YOLO algorithm model.In this process,video frames containing moving objects are first obtained by the moving object detection module.Second,the moving target will be recognized by the object of interest recognition module.Finally,the background decision module records and analyzes the detection results to obtain background model updates or result output.It detects moving objects without using traditional background modeling methods.Experiments based on theCDnet2014 dataset showthat our method improves the missed detection rate by 0.098% and the average inference speed per frame by 45.62%compared with the YOLO-based humanoid detection method.Furthermore,the method has superior performance in scenarios where target objects appear less frequently(substations,transmission lines,and hazardous areas).
