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

doi:10.1016/j.compeleceng.2022.108333

2022-10-01

PRE

Saravanan Chandrasekaran

Shamimul Qamar

DOI：10.1016/j.compeleceng.2022.108333

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摘要

En 中文

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.

Keyword:

Multi-object detection

Probabilistic Gaussian Mixture Model

Kernel convoluted moving window with

Kalman filter

Background suppression

Video surveillance

Video sequence