An Efficient Approach for Detecting and Classifying Moving Vehicles in a Video Based Monitoring System
AbstractMoving 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.
The author assigns to Engineering and Technology Journal with full title guarantee, all copyrights, rights in the nature of copyright, and all other intellectual property rights in the article throughout the world (present and future, and including all renewals, extensions, revivals, restorations and accrued rights of action). The Author represents that he/she is the author and proprietor of this Article and that this Article has not heretofore been published in any form. The Author warrants that he/she has obtained written permission and paid all fees for use of any literary or illustration material for which rights are held by others. The author agrees to hold the editor(s)/publisher harmless against any suit, demand, claim or recovery, finally sustained, by reason of any violation of proprietary right or copyright, or any unlawful matter contained in the submitted article.