Abstract: This paper implements three-way nested design to mark moving objects in a sequence of images. Algorithm performs object detection in the image motion analysis. The inter-frame changes (level-A) are marked as temporal contents, while the intra-frame variations identifies critical information. The spatial details are marked at two granular levels, comprising of level-B and level-C. The segmentation is performed using analysis of variance (ANOVA). This algorithm gives excellent results in situations where images are corrupted with heavy Gaussian noise ~N(0,100). The sample images are selected in four categories: ‘baseline’, ‘dynamic background’, ‘camera jitter’, and ‘shadows’. Results are compared with previously published results on four accounts: false positive rate (FPR), false negative rate (FNR), percentage of wrong classification (PWC), and an F-measure. The qualitative and quantitative results prove that the technique out performs the previously reported results by a significant margin.

Asim ur Rehman Khan, Muhammad Burhan Khan, Haider Mehdi and Syed Muhammad Atif Saleem, “Moving Object Detection in Highly Corrupted Noise using Analysis of Variance” International Journal of Advanced Computer Science and Applications(IJACSA), 10(6), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100629

@article{Khan2019,
title = {Moving Object Detection in Highly Corrupted Noise using Analysis of Variance},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100629},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100629},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {6},
author = {Asim ur Rehman Khan and Muhammad Burhan Khan and Haider Mehdi and Syed Muhammad Atif Saleem}
}