Moving Objects Detection Based on Frequency Domain

Abstract

In this research a proposed technique is used to enhance the frame difference technique performance for extracting moving objects in video file. One of the most effective factors in performance dropping is noise existence, which may cause incorrect moving objects identification. Therefore it was necessary to find a way to diminish this noise effect. Traditional Average and Median spatial filters can be used to handle such situations. But here in this work the focus is on utilizing spectral domain through using Fourier and Wavelet transformations in order to decrease this noise effect. Experiments and statistical features (Entropy, Standard deviation) proved that these transformations can stand to overcome such problems in an elegant way.

Yolo: تحليل تنافسي لخوارزميات الكشف عن الأشياء الحديثة للكشف عن عيوب الطرق باستخدام صور الطائرات بدون طيار

“…Different methods have been used for detecting objects, such as Fourier and Wavelet transformations, to reduce computational complexity and enhance moving object detection 6 . YOLO versions are state-of-the-art algorithm in object detection.…”

Overcoming camera instability problem for detecting and tracking moving objects in video using reduced data

“…The first step for each video surveillance system is the moving object detection [1]. Intuitively object detection is the preliminary stage for the successive tracking operation [2]- [4]. Employing human beings is inefficient way for monitoring places because of their limited capabilities and high cost demands as compared with the automated surveillance systems [5].…”