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Target Verification via Novel Adaptive Segmentation Used to Detect and Track Moving Objects

机译:通过新颖的自适应分段进行目标验证,用于检测和跟踪运动物体

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It is an original study to integrate the utmost activities and academic research. In this research, a new image segmentation (NIS) is present to search object information for initial target verification in the region of interest (ROI) area in global histogram. After that adaptive singular value decomposition (ASVD) is combined to suppress variation in lighting for color images. HSV color model integrates computer vision techniques made to fit the dynamic environments for object detection. In addition, several tracking algorithms are applied to estimate and track the activity data. Experimental results show that the objects could successfully detect and track the sequence of images, performance and the tracking rate in accordance with accurate Kalman filter (HSV) which is better than the other algorithms.
机译:这是一项将最大的活动与学术研究相结合的原创研究。在这项研究中,提出了一种新的图像分割(NIS)来搜索对象信息,以在全局直方图的感兴趣区域(ROI)区域中进行初始目标验证。之后,将自适应奇异值分解(ASVD)进行组合以抑制彩色图像的照明变化。 HSV颜色模型集成了计算机视觉技术,以适应动态环境进行对象检测。另外,应用了几种跟踪算法来估计和跟踪活动数据。实验结果表明,该对象能够根据精确的卡尔曼滤波器(HSV)成功检测并跟踪图像序列,性能和跟踪速率,优于其他算法。

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