An efficient approach for detecting and classifying moving vehicles in a video based monitoring system

الملخص EN

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 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.