Unusual Activity Detection in Surveillance Video using Machine Learning and Discriminative Deep Belief Network Techniques - Baby, M Petchiammal; Santhiya, S; RathaJeyalakshmi, T (International Journal of Computer Sciences and Engineering, 2025)
In recent years, video police work systems are typically adopted more or less the planet as security issues and their low hardware price. Anomaly detection is one in all the analysis areas within the field of video police work. During this study, totally different existing cluster primarily based, like techniques EM bunch and classification primarily based anomaly detection techniques in video police work square measure mentioned. The video closed-circuit television includes background modeling, object detection, object following, activity recognition and classification. Recently, the machine learning primarily based anomaly detection techniques plays a significant role within the classification of the events into traditional and abnormal events. The new approaches just like the grouping of Convolution Neural Network and repeated Neural Network and cascade deep learning square measure the strong algorithms for big datasets. The options so extracted square measure fed to a Discriminative Deep Belief Network. Labeled videos of some uncertain activities also are fed to the DDBN and their options also are extracted. Then the options extracted exploitation Convolution Neural Network square measure compared against these options extracted from the labeled sample video of classified suspicious actions employing a Discriminative Deep Belief Network (DDBN) and varied suspicious actions square measure detected from the given video.