Object Location Prediction Based on Motion Estimation with Application on Color-Based Foreground Object Detection
Date Issued
2007
Date
2007
Author(s)
Chiang, Ching-Chun
DOI
en-US
Abstract
Many computer vision and machine vision applications employ some foreground detection methods as the first stage for detecting object location. Many characteristics of image data have been used to segment images into background and foreground elements. Motion is effective information for detecting moving objects in two continuous images.
Although motion is helpful to detect foreground objects, it requires a heavy computational load when detecting all motions of an image. In previous applications, some fast search algorithms are proposed to reduce the computational load of motion estimation. In fact, not all motions are important in an image. Only the foreground object motion is required in a foreground detection system. The background motion is not necessary for detecting foreground, and it means that the motion estimation process has no need to be applied in the background area.
In this research, a method is proposed for predicting foreground object location in a video. The method uses both motion and traffic density to predict object location in an input image. Motion is obtained by motion estimation, and traffic density is obtained by the analysis of historical detection results.
A program of foreground detection is designed to verify the prediction method. The prediction capabilities with moving and size-changing object are explained by the experiments with some special videos. Finally, the advantages of using the prediction method are illustrated through the experiment with three different input videos.
Subjects
前景偵測
物體位置預測
影像處理
影像分析
foreground detection
object location prediction
image processing
image analysis
Type
thesis
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