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Extraction of Visual Features from Video Sequences for Better Visual Analysis

机译:从视频序列中提取视觉特征以进行更好的视觉分析

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摘要

Video have a basic and non basic features, where basic features includes e. g. color, shape, size and non basic features include orientation of a image. Whereas Video Sequences is a series of shots/frames on a subject that are edited together to tell a story. Visual features describes the details about the image contents, which are used in various applications like, visual search, object recognition, image registration and object tracking. Many visual analysis task requires the features to be transmitted, thus it calls for the different coding algorithms to attain a target level of efficiency. Here an effort has been taken to implement a coding algorithm for local features extraction such as SIFT (Scale Invariant Feature Transform). The first stage comprises of using the SIFT algorithm property to find the `point of interest' of an image. Further the use Kalman Filter algorithm is done as an application purpose of motion based single or multiple object detection and tracking.
机译:视频具有基本和非基本功能,其中基本功能包括e。 G。颜色,形状,大小和非基本特征包括图像的方向。而“视频序列”是对主题的一系列镜头/帧,它们被一起编辑以讲述一个故事。视觉功能描述有关图像内容的详细信息,这些内容可在各种应用程序中使用,例如视觉搜索,对象识别,图像配准和对象跟踪。许多视觉分析任务要求传递特征,因此需要不同的编码算法来达到目标​​效率水平。在此,已努力实现用于局部特征提取的编码算法,例如SIFT(尺度不变特征变换)。第一阶段包括使用SIFT算法属性来找到图像的“兴趣点”。进一步地,使用卡尔曼滤波算法作为基于运动的单个或多个对象检测和跟踪的应用目的。

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