Multi-modal cyber security based object detection by classification using deep learning and background suppression techniques

• Published in: Computers & electrical engineering Vol. 103; p. 108333
• Main Authors: Srinivas, Kalyanapu, Singh, Laxman, Chavva, Subba Reddy, Dappuri, Bhasker, Chandrasekaran, Saravanan, Qamar, Shamimul
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-10-2022
• Subjects: Background suppression; Kernel convoluted moving window with Kalman filter; Multi-object detection; Probabilistic Gaussian Mixture Model; Video sequence; Video surveillance; Video surveillance; Video sequence; Probabilistic Gaussian Mixture Model; Multi-object detection; Kernel convoluted moving window with Kalman filter; Background suppression
• ISSN: 0045-7906; 1879-0755
• DOI: 10.1016/j.compeleceng.2022.108333

•The network cyber security has to be enhanced and the proposed technique implemented with cyber security control system.•This research has also performed multiple moving objects tracking using Kernel's convoluted moving window with Kalman filter (KCMW_KF).•Foreground object recognition in video is critical in many computer vision applications and automated video surveillance systems.•Object tracking establishes the correlation between objects in a video sequence's succeeding frames.
Foreground object recognition in video is critical in many computer vision applications and automated video surveillance systems. Object detection and tracking are critical steps in navigation, object recognition and surveillance schemes. Object detection is process of separating foreground and background items in photographs. In this paper, we proposes a framework for achieving these tasks with the  enhanced cyber security control facility. This proposed algorithmperformed in 2 stages: multi-object detection utilizing the Cyber secure Probabilistic Gaussian Mixture Model (Cy_SPGMM) and background suppression and another stage is multiple moving objects tracking utilizing Kernel convoluted moving window with Kalman filter (KCMW_KF). It can, however, deal with a variety of video sequences in the MOT 20 dataset. The experimental findings reveal that the proposed algorithm detects and tracks foreground objects in complex and dynamic scenarios with high accuracy, robustness, and efficiency. This method also produces smoothened images without noise.
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