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<p>Multi-class SVM based C3D Framework for Real-Time Anomaly Detection</p>

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Title Statement <p>Multi-class SVM based C3D Framework for Real-Time Anomaly Detection</p>
 
Added Entry - Uncontrolled Name Thotakura, Vishnu Priya; School of Electronics Engineering, VIT-AP University, Amaravati 522 237, Andhra Pradesh, India
Purnachand, N ; School of Electronics Engineering, VIT-AP University, Amaravati 522 237, Andhra Pradesh, India
 
Uncontrolled Index Term Convolutional neural network, Multiple instance learning, Region of interest, Support vector machine
 
Summary, etc. <p>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.</p>
 
Publication, Distribution, Etc. Journal of Scientific & Industrial Research
2022-03-14 19:43:40
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/JSIR/article/view/50480
 
Data Source Entry Journal of Scientific & Industrial Research; ##issue.vol## 81, ##issue.no## 02 (2022): Journal of Scientific and Industrial Research
 
Language Note en