Research On Abnormal Event Detection Algorithm Based On Deep Learning

Abnormal event detection is a hot research topic in the field of computer vision.Its research results are widely used in intelligent transportation,intelligent urban management,intelligent video surveillance and other fields.However,frequent changes in the detection background,abnormal definition of the abnormalities,and complicated trajectories of crowd movements all affect the accuracy of abnormal event detection.This makes abnormal event detection a major difficulty in computer vision research.Aiming at the problem of low detection accuracy of abnormal events in multi-scenes and multi-abnormal situations,the processing data sets method is improved to solve the problem that the abnormal features are not prominent.Based on this,an abnormal event detection algorithm based on deep learning is proposed to improve the accuracy of the detection of abnormal events.In terms of data sets,this paper integrates a variety of image processing techniques to process the complex scene data in response to the problem that the data set's initial anomaly features are not prominent.The process is divided into ROI processing,down sampling processing and pyramid LK optical flow processing.In addition,taking into account that the data sets can not reach the amount of data required for deep learning training,this paper uses the three data enhancement methods of flip transformation,reflection transformation,and noise perturbation to extend the samples,avoid over-fitting problems caused by insufficient training sets during network training.In terms of algorithms,an abnormal event detection algorithm based on deep learning is proposed,that is it trained using a deep learning method.Use GoogleNet`s advantages of deeper and wider network structure,apply it in the detection of abnormal events,besides,in the training of the model,in order to prevent the occurrence of over-fitting of the network,batch normalization processing of the data,and using Dropout to randomly ignore some neurons and at the last layer of the network,replacing the whole with global average pooling Connections,make the network model more suitable for the detection of abnormal events in this article.Finally,test the trained model with PKU-SVD-B test set,and verify the algorithm of abnormal event detection based on deep learning proposed in this paper.Under the premise of adapting to different scenarios,various abnormal events are detected.The high accuracy rate indicates that the trained model has a strong ability to generalize abnormal event detection.