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Multi-class SVM based C3D Framework for Real-Time Anomaly Detection

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Title Multi-class SVM based C3D Framework for Real-Time Anomaly Detection
 
Creator Thotakura, Vishnu Priya
Purnachand, N
 
Subject Convolutional neural network
Multiple instance learning
Region of interest
Support vector machine
 
Description 166-172
The conventional multi-class anomaly detection models are independent of noise elimination and feature segmentation due to large number of feature space and training images. As the number of human anomaly classes is increasing, it is difficult to find the multi-class anomaly due to high computational memory and time. In order to improve the multi-class human anomaly detection process, an advanced multi-class segmentation-based classification model is designed and implemented on the different human anomaly action databases. In the proposed model, a hybrid filtered based C3D framework is used to find the essential key features from the multiple human action data and an ensemble multi-class classification model is implemented in order to predict the new type of actions with high accuracy. Experimental outcomes proved that the proposed multi- class classification C3D model has better human anomaly detection rate than the traditional multi-class segmentation models.
 
Date 2022-02-04T11:34:23Z
2022-02-04T11:34:23Z
2022-02
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/59082
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.81(02) [Feburary 2022]