Low visibility conditions,particularly those caused by fog,significantly affect road safety and reduce drivers’ability to see ahead clearly.The conventional approaches used to address this problem primarily rely on i...Low visibility conditions,particularly those caused by fog,significantly affect road safety and reduce drivers’ability to see ahead clearly.The conventional approaches used to address this problem primarily rely on instrument-based and fixed-threshold-based theoretical frameworks,which face challenges in adaptability and demonstrate lower performance under varying environmental conditions.To overcome these challenges,we propose a real-time visibility estimation model that leverages roadside CCTV cameras to monitor and identify visibility levels under different weather conditions.The proposedmethod begins by identifying specific regions of interest(ROI)in the CCTVimages and focuses on extracting specific features such as the number of lines and contours detected within these regions.These features are then provided as an input to the proposed hierarchical clusteringmodel,which classifies them into different visibility levels without the need for predefined rules and threshold values.In the proposed approach,we used two different distance similaritymetrics,namely dynamic time warping(DTW)and Euclidean distance,alongside the proposed hierarchical clustering model and noted its performance in terms of numerous evaluation measures.The proposed model achieved an average accuracy of 97.81%,precision of 91.31%,recall of 91.25%,and F1-score of 91.27% using theDTWdistancemetric.We also conducted experiments for other deep learning(DL)-based models used in the literature and compared their performances with the proposed model.The experimental results demonstrate that the proposedmodel ismore adaptable and consistent compared to themethods used in the literature.The proposedmethod provides drivers real-time and accurate visibility information and enhances road safety during low visibility conditions.展开更多
With a formidable antiterrorism team, Beijing's top cops are ready to deal with any security incidents that might take place during the 2008 Olympics. This assurance was given by Lu Shimin, Deputy Director General...With a formidable antiterrorism team, Beijing's top cops are ready to deal with any security incidents that might take place during the 2008 Olympics. This assurance was given by Lu Shimin, Deputy Director General of the Beijing Public Security Bureau, at a展开更多
文摘Low visibility conditions,particularly those caused by fog,significantly affect road safety and reduce drivers’ability to see ahead clearly.The conventional approaches used to address this problem primarily rely on instrument-based and fixed-threshold-based theoretical frameworks,which face challenges in adaptability and demonstrate lower performance under varying environmental conditions.To overcome these challenges,we propose a real-time visibility estimation model that leverages roadside CCTV cameras to monitor and identify visibility levels under different weather conditions.The proposedmethod begins by identifying specific regions of interest(ROI)in the CCTVimages and focuses on extracting specific features such as the number of lines and contours detected within these regions.These features are then provided as an input to the proposed hierarchical clusteringmodel,which classifies them into different visibility levels without the need for predefined rules and threshold values.In the proposed approach,we used two different distance similaritymetrics,namely dynamic time warping(DTW)and Euclidean distance,alongside the proposed hierarchical clustering model and noted its performance in terms of numerous evaluation measures.The proposed model achieved an average accuracy of 97.81%,precision of 91.31%,recall of 91.25%,and F1-score of 91.27% using theDTWdistancemetric.We also conducted experiments for other deep learning(DL)-based models used in the literature and compared their performances with the proposed model.The experimental results demonstrate that the proposedmodel ismore adaptable and consistent compared to themethods used in the literature.The proposedmethod provides drivers real-time and accurate visibility information and enhances road safety during low visibility conditions.
文摘With a formidable antiterrorism team, Beijing's top cops are ready to deal with any security incidents that might take place during the 2008 Olympics. This assurance was given by Lu Shimin, Deputy Director General of the Beijing Public Security Bureau, at a
