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The detection and tracking of moving objects attracted a lot of concern because of the vast computer vision applications. This paper proposes a new algorithm based on several methods for identifying, detecting, and tracking an object in order to develop an effective and efficient system in several applications. This algorithm has three main parts: the first part for background modeling and foreground extraction, the second part for smoothing, filtering and detecting moving objects within the video frame and the last part includes tracking and prediction of detected objects. In this proposed work, a new algorithm to detect moving objects from video data is designed by the Fast Principle Component Purist (FPCP). Then we used an optimal filter that performs well to reduce noise through the median filter. The Fast Region-convolution neural networks (Fast-RCNN) is used to add smoothness to the spatial identification of objects and their areas. Then the detected object is tracked by Kalman Filter. Experimental results show that our algorithm adapts to different situations and outperforms many existing algorithms.

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This paper study the model reduction procedures used for the reduction of large-scale dynamic models into a smaller one through some sort of differential and algebraic equations. A confirmed relevance between these two models exists, and it shows same characteristics under study. These reduction procedures are generally utilized for mitigating computational complexity, facilitating system analysis, and thence reducing time and costs. This paper comes out with a study showing the impact of the consolidation between the Bacterial-Foraging (BF) and Modified particle swarm optimization (MPSO) for the reduced order model (ROM). The proposed hybrid algorithm (BF-MPSO) is comprehensively compared with the BF and MPSO algorithms; a comparison is also made with selected existing techniques.

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