Browsing by Subject "Moving object detection"

Now showing 1 - 5 of 5

Open Access
Moving object detection using adaptive subband decomposition and fractional lower-order statistics in video sequences
(Elsevier, 2002) Bagci, A. M.; Yardimci, Y.; Çetin, A. Enis
In this paper, a moving object detection method in video sequences is described. In the first step, the camera motion is eliminated using motion compensation. An adaptive subband decomposition structure is then used to analyze the motion compensated image. In the "low-high" and "high-low" subimages moving objects appear as outliers and they are detected using a statistical detection test based on fractional lower-order statistics. It turns out that the distribution of the subimage pixels is almost Gaussian in general. On the other hand, at the object boundaries the distribution of the pixels in the subimages deviates from Gaussianity due to the existence of outliers. By detecting the regions containing outliers the boundaries of the moving objects are estimated. Simulation examples are presented. © 2002 Elsevier Science B.V. All rights reserved.
Open Access
Real-time detection, tracking and classification of multiple moving objects in UAV videos
(IEEE, 2017-11) Baykara, Hüseyin Can; Bıyık, Erdem; Gül, Gamze; Onural, Deniz; Öztürk, Ahmet Safa; Yıldız, İlkay
Unnamed Aerial Vehicles (UAVs) are becoming increasingly popular and widely used for surveillance and reconnaissance. There are some recent studies regarding moving object detection, tracking, and classification from UAV videos. A unifying study, which also extends the application scope of such previous works and provides real-Time results, is absent from the literature. This paper aims to fill this gap by presenting a framework that can robustly detect, track and classify multiple moving objects in real-Time, using commercially available UAV systems and a common laptop computer. The framework can additionally deliver practical information about the detected objects, such as their coordinates and velocities. The performance of the proposed framework, which surpasses human capabilities for moving object detection, is reported and discussed.
Open Access
Small moving object detection in video sequences
(IEEE, 2000-06) Zaibi, Rabi; Çetin, A. Enis; Yardımcı, Y.
In this paper, we propose a method for detection of small moving objects in video. We first eliminate the camera motion using motion compensation. We then use an adaptive predictor to estimate the current pixel using neighboring pixels in the motion compensated image and, in this way, obtain a residual error image. Small moving objects appear as outliers in the residual image and are detected using a statistical Gaussianity detection test based on higher order statistics. It turns out that in general, the distribution of the residual error image pixels is almost Gaussian. On the other hand, the distribution of the pixels in the residual image deviates from Gaussianity in the existence of outliers. Simulation examples are presented.
Open Access
Small moving object detection using adaptive subband decomposition and fractional lower order statistics in video sequences
(2001-07-08) Bağci, A.Murat; Yardımcı, Y.; Çetin, A. Enis
In this paper, a small moving object method detection method in video sequences is described. In the first step, the camera motion is eliminated using motion compensation. An adaptive subband decomposition structure is then used to analyze the motion compensated image. In the highband subimages moving objects appear as outliers and they are detected using a statistical detection test based on lower order statistics. It turns out that in general, the distribution of the residual error image pixels is almost Gaussian. On the other hand, the distribution of the pixels in the residual image deviates from Gaussianity in the existence of outliers. By detecting the regions containing outliers the boundaries of the moving objects are estimated. Simulation examples are presented.
Open Access
Small moving object detection using adaptive subband decomposition in video sequences
(SPIE, 2000) Zaibi, Rabi; Çetin, A. Enis; Yardımcı, Y. C.
In this paper, a small moving object method detection method in video sequences is described. In the first step, the camera motion is eliminated using motion compensation. An adaptive subband decomposition structure is then used to analyze the motion compensated image. In the 'low-high' and 'high-low' subimages small moving objects appear as outliers and they are detected using a statistical Gaussianity detection test based on higher order statistics. It turns out that in general, the distribution of the residual error image pixels is almost Gaussian. On the other hand, the distribution of the pixels in the residual image deviates from Gaussianity in the existence of outliers. Simulation examples are presented.