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首页> 外文期刊>American journal of applied sciences >Multiple Vehicle Tracking using Adaptive Gaussian Mixture Model and Kalman Filter | Science Publications
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Multiple Vehicle Tracking using Adaptive Gaussian Mixture Model and Kalman Filter | Science Publications

机译：自适应高斯混合模型和卡尔曼滤波的多车辆跟踪科学出版物

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> >Based on data from the Central Statistics Agency (BPS) of the Republic of Indonesia in catalog Crime Statistics in 2014, the number of crimes of theft of motor vehicles has increased since 2010 until 2013. Recorded in 2013 the number reached 42.508 cases of theft. In this study, we made a prototype application using Adaptive Gaussian Mixture Models algorithms and the Kalman Filter to detect the movement of the vehicle in order to prevent vehicle theft. Adaptive Gaussian Mixture Models were used for image segmentation foreground and background, while the Kalman Filter was used to track the vehicle. Stages in this study consisted of two phases, namely the manufacture of prototype and testing of prototype applications. Testing was done by observing the resource usage of memory (RAM) and the processor when the application was executed and the speed and degree of vehicle motion detection accuracy. The test results showed that the prototype application using Adaptive Gaussian Mixture Model and Kalman Filter had an accuracy rate of 90% and a high speed in detecting motion with the use of vehicles under 65000 KB RAM and processor work load below 27% on condition vehicles that have mutual occlusion.
机译： > >根据印度尼西亚共和国中央统计局（BPS）2014年犯罪统计目录中的数据，自2010年至2013年，盗窃汽车的犯罪数量有所增加。 2013年，盗窃案数量达到42.508起。在这项研究中，我们使用自适应高斯混合模型算法和卡尔曼滤波器在原型应用中进行了检测，以防止车辆被盗。自适应高斯混合模型用于图像分割的前景和背景，而卡尔曼滤波器用于跟踪车辆。该研究的阶段包括两个阶段，即原型的制造和原型应用程序的测试。通过观察执行应用程序时内存（RAM）和处理器的资源使用情况以及车辆运动检测精度的速度和程度来进行测试。测试结果表明，使用自适应高斯混合模型和卡尔曼滤波器的原型应用程序在使用65000 KB RAM以下的车辆和处理器工作负荷在27％以下的情况下，能够以90％的准确率和高速检测运动。相互遮挡。

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