An Efficient Dimension Reduction based Fusion of CNN and SVM Model for Detection of Abnormal Incident in Video Surveillance

Abstract

Performing dimensionality reduction in the camera captured images without any loss is remaining as a big challenge in image processing domain. Generally, camera surveillance system is consuming more volume to store video files in the memory. The normally used video stream will not be sufficient for all the sectors. The abnormal conditions should be analyzed carefully for identifying any crime or mistakes in any type of industries, companies, shops, etc. In order to make it comfortable to analyze the video surveillance within a short time period, the storage of abnormal conditions of the video pictures plays a very significant role. Searching unusual events in a day can be incorporated into the existing model, which will be considered as a supreme benefit of the proposed model. The massive video stream is compressed in preprocessing the proposed learning method is the key of our proposed algorithm. The proposed efficient deep learning framework is based on intelligent anomaly detection in video surveillance in a continuous manner and it is used to reduce the time complexity. The dimensionality reduction of the video captured images has been done by preprocessing the learning process. The proposed pre-trained model is used to reduce the dimension of the extracted image features in a sequence of video frames that remain as the valuable and anomalous events in the frame. The selection of special features from each frame of the video and background subtraction process can reduce the dimension in the framework. The proposed method is a combination of CNN and SVM architecture for the detection of abnormal conditions at video surveillance with the help of an image classification procedure. This research article compares various methods such as Journal of Soft Computing Paradigm (JSCP) (2021) Vol.03/ No.02 Pages: 55-69 http://irojournals.com/jscp/ DOI: https://doi.org/10.36548/jscp.2021.2.001 56 ISSN: 2582-2640 (online) Submitted:6.03.2021 Revised: 30.03.2021 Accepted: 21.04.2021 Published: 10.05.2021 background subtraction (BS), temporal feature extraction (TFE), and single classifier classification methods.