TY  - JOUR
T1  - Performance Evaluation of Support Vector Machines (SVM) and Convolution Neural
Networks (CNN) for Video Tampering Classification
AU - Komal, S.K. AU - Chandrashekar, Puneeth AU - Rekha, B.S. AU - Srinivasan, G.N. 
JO  - International Journal of Soft Computing
VL  - 14
IS  - 3
SP  - 53
EP  - 60
PY  - 2019
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2019.53.60
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2019.53.60
KW  - Support vector machiness
KW  -convolutional neural networks
KW  -tensorflow
KW  -surveillance activities
KW  -performance
AB  - Intelligent video surveillance system are
extensively used in each and every sector of business.
Ranging from small shops to safety systems, surveillance
has become an integral part. In these fielded systems, a
variety of factors can cause camera obstructions and
persistent view change. The view change may adversely
affect their performance. Examples include intentional
blockage, noise, frame freeze, etc. which might warrant
alarms. Considering the fact that the intelligent
surveillance system is with very less human intervention,
it is important to efficiently classify the tampered video.
Analysis of the tampered videos helps in further scene
investigation. The goal of the project is to use Support
Vector Machines (SVM) a machine learning technique
which classifies the real-time videos based on features
extracted. The features selected are histogram gradients,
HSV (Hue Saturation Value) and RGB (Red Blue Green)
for the color based classification and edges (edge weight
and direction) for the texture based classification. Further
improvements are done using a deep learning technique
such as CNN. Convolution neural networks make use of
large amount of training data and use tensorflow
framework for classification. The system accepts video
inputs in mp3 or avi format. The output is the
classification of tampered videos and alarm generation.
Comparison between the two methodologies is done.
Support vector machines gives an accuracy of 75% and
convolutional neural networks give accuracy of 93%. The
system is very useful to monitor all the surveillance
activities.
ER  -