Optimal object detection and tracking in occluded video using DNN and gravitational search algorithm

Abstract

Moving object tracking is an effective optimization procedure based on the impermanent relevant information associated with the original frames. Suggesting a method with efficient accuracy in convoluted atmospheres is a difficulty for scientists in the area of research study. In this research, powerful object detection and movement tracking videos are proposed. Here, we are considering the input video sequence PETS and Hall monitor videos. Initially, the background and foreground separations are done by modified kernel fuzzy c-means algorithm. The object detection and tracking are done by gravitational search algorithm-based deep belief neural network. The implementation will be in MATLAB. The effectiveness of the recommended strategy is assessed with means of precision, recall, F-measure, FPR, FNR, PWC, FAR, similarity, specificity, and accuracy. From the experimental results, the proposed work outperforms the state of artwork. Here, the proposed method attains maximum precision and recall value for both PETS and Hall monitor video when compared to the existing algorithm.