Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Background Subtraction


Comparative Analysis of GMM, KNN, and ViBe Background Subtraction Algorithms Applied in Dynamic Background Scenes of Video Surveillance System

Maryam A. Yasir; Yossra H. Ali

Engineering and Technology Journal, 2022, Volume 40, Issue 4, Pages 617-626
DOI: 10.30684/etj.v40i4.2154

Background subtraction is the most prominent technique applied in the domain of detecting moving objects. However, there is a wide range of different background subtraction models. Choosing the best model that addresses a number of challenges is still a vital research area.
Therefore, in this article we present a comparative analysis of three promising algorithms used in this domain, GMM, KNN and ViBe. CDnet 2014 is the benchmark dataset used in this analysis with several quantitative evaluation metrics like precession, recall, f-measures, false positive rate, false negative rate and PWC. In addition, qualitative evaluations are illustrated in snapshots to depict the visual scenes evaluation. ViBe algorithm outperform other algorithms for overall evaluations.

An Efficient Approach for Detecting and Classifying Moving Vehicles in a Video Based Monitoring System

Sajidah S. Mahmood; Laith J. Saud

Engineering and Technology Journal, 2020, Volume 38, Issue 6, Pages 832-845
DOI: 10.30684/etj.v38i6A.438

Moving objects detection, type recognition, and traffic analysis in video-based surveillance systems is an active area of research which has many applications in road traffic monitoring. This paper is on using classical approaches of image processing to develop an efficient algorithm for computer vision based on traffic surveillance system that can detect and classify moving vehicles, besides serving some other traffic analysis issues like finding vehicles speed and heading, tracking specified vehicles, and finding traffic load. The algorithm is designed to be flexible for modification to fulfill the changes in design objectives, having limited computation time, giving good accuracy, and serves inexpensive implementation. A 92% of success is achieved for the considered test, with the missed cases being abnormal that are not defined to the algorithm. The computation time, with a platform (hardware and software) dependent, the algorithm took to produce results was parts of milliseconds. A CNN based deep learning classifier was built and evaluated to judge the feasibility of involving a modern approach in the design for the targeted aims in this work. The modern NN based deep learning approach is very powerful and represents the choice for many very sophisticated applications, but when the purpose is restricted to limited requirements, as it is believed the case is here, the reason will be to use the classical image processing procedures. In making choice, it is important to consider, among many things, accuracy, computation time, and simplicity of design, development, and implementation.