Sketch and Size Orient Malicious Activity Monitoring for Efficient Video Surveillance Using CNN

Towards malicious activity monitoring in organizations, there exist several techniques and suffer with poor accuracy. To handle this issue, an efficient Sketch and Size orient malicious activity monitoring (SSMAM) is presented in this article. The model captures the video frames and performs segmentation to extract the features of frames as shapes and size. The video frames are enhanced for its quality by applying High Level Intensity Analysis algorithm. The quality improved image has been segmented with Color Quantization Segmentation. Using the segmented image, the feature are extracted and applied with scaling and rotation for different number of size and angle. Such features extracted have been trained with convolution neural network. The CNN model is designed to perform convolution on two levels and pooling as well. At the test phase, the method extract the same set of features and performs convolution to obtain same set of feature lengths and the neurons are designed computes the value of Sketch Support Measure (SSM) towards various class of activity. According the value of SSM, the method classifies the user activity towards efficient video surveillance. The proposed approach improves the performance in activity monitoring and video surveillance.