Motion detection, tracking and classification for automated Video Surveillance

Neha Gaba, Neelam Barak, Shipra Aggarwal
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引用次数: 17

Abstract

Moving object identification and tracking motion is the base source to extract vital information regarding moving objects from sequences in continuous image based surveillance systems. An advanced approach to motion detection for automatic video analysis has been presented in the paper. This achieves complete detection of moving object which is robust against of changes in brightness, dynamic variations in the surrounding environment and noise from the background. The proposed method is a pixel dependent and non-parametrized approach that is based on first frame to build the model. The detection of the foreground which represents the object and background which is the surrounding of the environment starts once the subsequent frame is captured. It utilizes unique tracking methodology that identifies and eliminates the ghost object from dissolving into the background of the frame. The proposed algorithm has been test implemented on several open source videos by imposing single set of variables to overcome shortcomings of relevant and recently developed techniques.
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用于自动视频监控的运动检测、跟踪和分类
在基于连续图像的监控系统中,运动目标识别和运动跟踪是提取运动目标重要信息的基础。本文提出了一种用于自动视频分析的运动检测方法。这实现了对运动物体的完整检测,对亮度变化,周围环境的动态变化和背景噪声具有鲁棒性。该方法是一种基于第一帧的非参数化像素依赖方法。一旦捕捉到后续帧,就会开始检测代表物体的前景和作为环境周围的背景。它利用独特的跟踪方法来识别和消除幽灵物体溶解到帧的背景中。所提出的算法已经在几个开源视频上进行了测试,通过施加单一变量集来克服相关和最近开发的技术的缺点。
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