Pixel Level Image Fusion in Moving objection Detection and Tracking with Machine Learning

It is not feasible for a single image sensor to convey all of the information essential to comprehend a circumstance thoroughly. The output of many image sensors combined in one place would supply more accurate or comprehensive information on the topic at hand. In recent years, multi-sensor fusion has emerged in the academic world as an emerging topic that has the potential to produce beneficial results. This is because it can aggregate the data from several different sensors. One of the primary objectives is to devise various methods for combining kinematic and visual data to track a moving object. These methods should allow us to achieve this aim. This article looks into the intricacies of various techniques to evaluate the current condition of a target and explores the outcomes of those approaches. These sorts of methods include, for instance, the Kalman filter and its expanded version, the extended Kalman filter. The study of the proposed work is to demonstrate the specifics of the development of an interacting multiple-model Kalman filter to monitor the performance of the moving target in response to a wide variety of tuning parameters. The proposed technique includes the Principal Component Analysis and spatial frequency to integrate the hazy images that were all shot with the same sensor modalities. This action was taken to achieve the aimed-for outcome. The effectiveness of the fusion is evaluated based on the results of several distinct metrics.