一种基于改进区域生长算法的运动目标检测与跟踪系统

G. Sujatha, Valli Kumari Vatsavayi
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引用次数: 3

摘要

本研究的最终目标是消除目前存在的限制,提供增强的视频目标检测和跟踪。虽然在早期的工作中实现了较高的视频目标检测和跟踪性能,但计算时间较长。因此,我们需要提出一种新的视频目标检测和跟踪技术,以最大限度地降低计算复杂度。我们提出的技术包括预处理、分割、特征提取、背景减去和孔填充五个阶段。最初,数据库中的视频片段被分割成帧。然后进行预处理以去除噪声,在此阶段使用自适应中值滤波器去除噪声。然后利用改进的区域增长算法对预处理后的图像进行分割。对分割后的图像进行特征提取阶段,从分割后的图像和背景图像中提取出多个特征,对得到的特征值进行比较,得到最优值,从而得到前景图像。由于前景图像中含有孔洞和不连续面,因此对前景图像进行侵蚀和膨胀的形态学运算,以填充孔洞并准确获取目标。因此,在这个阶段跟踪移动的物体。该方法将在MATLAB平台上应用,并将结果与现有技术进行研究和比较,以揭示新型视频目标检测与跟踪技术的性能。
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AN INNOVATIVE MOVING OBJECT DETECTION AND TRACKING SYSTEM BY USING MODIFIED REGION GROWING ALGORITHM
The ultimate goal of this study is to afford enhanced video object detection and tracking by eliminating the limitations which are existing nowadays. Although high performance ratio for video object detection and tracking is achieved in the earlier work it takes more time for computation. Consequently we are in need to propose a novel video object detection and tracking technique so as to minimize the computational complexity. Our proposed technique covers five stages they are preprocessing, segmentation, feature extraction, background subtraction and hole filling. Originally the video clip in the database is split into frames. Then preprocessing is performed so as to get rid of noise, an adaptive median filter is used in this stage to eliminate the noise. The preprocessed image then undergoes segmentation by means of modified region growing algorithm. The segmented image is subjected to feature extraction phase so as to extract the multi features from the segmented image and the background image, the feature value thus obtained are compared so as to attain optimal value, consequently a foreground image is attained in this stage. The foreground image is then subjected to morphological operations of erosion and dilation so as to fill the holes and to get the object accurately as these foreground image contains holes and discontinuities. Thus the moving object is tracked in this stage. This method will be employed in MATLAB platform and the outcomes will be studied and compared with the existing techniques so as to reveal the performance of the novel video object detection and tracking technique.
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