Intelligent Technique for Moving Object Detection from Problematic Video Captured through Camera Sensor

Aims The significant aim of the proposed work is to develop an adaptive method to compute the threshold during run-time and update it adaptively for each pixel in the testing phase. It classifies motion-oriented pixels from the scene for moving objects using background subtraction and enhances using post-processing.Background According to the huge demand for surveillance system, society is looking towards an intelligent video surveillance system that detect and track moving objects from video captured through a surveillance camera. So, it is very crucial and highly recommended throughout the globe in numerous domains such as video-based surveillance, healthcare, transportation, and many more. Practically, this research area faces lots of challenging issues such as illumination variation, cluttered background, camouflage, etc. So, this paper has developed an adaptive background subtraction method to handle such challenging problems.Objective To focus and study the problematic video data captured through the camera sensor. To handle challenging issues available in real-time video scenes. To develop a background subtraction method and update the background model adaptively for moving object detection.Objective To focus and study the problematic video data captured through the camera sensor. To handle challenging issues available in real-time video scenes. To develop a background subtraction method and update the background model adaptively for moving object detection.Objective To focus and study the problematic video data captured through the camera sensor. To handle challenging issues available in real-time video scenes. To develop a background subtraction method and update the background model adaptively for moving object detection.Methods The proposed method has been accomplished using the following sections: Background model construction Automatic generation of threshold Background subtraction Maintenance of background modelMethods The proposed method has been accomplished using the following sections: Background model construction Automatic generation of threshold Background subtraction Maintenance of background modelMethods The proposed method has been accomplished using the following sections: Background model construction Automatic generation of threshold Background subtraction Maintenance of background modelMethods The proposed method has been accomplished using the following sections: Background model construction Automatic generation of threshold Background subtraction Maintenance of background modelMethods The proposed method has been accomplished using the following sections: Background model construction Automatic generation of threshold Background subtraction Maintenance of background modelResults The qualitative analysis of the proposed work is experimented with publicly available datasets and compared with considered state-of-the-art methods. In this work, library sequence (thermal data) of CDNET and other color video frame sequences Foreground aperture, Waving Tree and Camouflage are considered from Microsoft's Wallflower. The quantitative values depicted in Table-1. This work demonstrate the better performance of the proposed method as compared to state-of-the-art methods. It also generates better outcomes and handles the problem of a dynamic environment and illumination variation.Conclusion Currently, the world is demanding computer vision-based security and surveillance-based applications for society. This work has provided a method for the detection of moving information using an adaptive method of background subtraction approach for moving object detection in video scenes. The performance evaluation depicts better average results as compared to considered peer methods.