【摘 要】
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With the increasing number of vehicles,traffic accidents pose a great threat to human lives.Hence,aiming at reducing the occurrence of traffic ac-cidents,this paper proposes an algorithm based on a deep convolutional neural network and a random for-est to
【机 构】
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School of Communication and Information Engineering,Nanjing University of Posts and Telecommunicatio
论文部分内容阅读
With the increasing number of vehicles,traffic accidents pose a great threat to human lives.Hence,aiming at reducing the occurrence of traffic ac-cidents,this paper proposes an algorithm based on a deep convolutional neural network and a random for-est to predict accident risks.Specifically,the proposed algorithm includes a feature extractor and a feature classifier,where the former extracts key features using a convolutional neural network and the latter outputs a probability value of traffic accidents using a random forest with multiple decision trees,which indicates the degree of accident risks.Simulations show that the proposed algorithm can achieve higher performance in terms of the Area Under the Curve (AUC) of the Re-ceiver Characteristic Operator as well as accuracy than the existing algorithms based on the Adaboost or the pure convolutional neural networks.
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