In this paper,a non-contact auto-focusing method is proposed for the essential function of auto-focusing in mobile devices.Firstly,we introduce an effective target detection method combining the 3-frame difference alg...In this paper,a non-contact auto-focusing method is proposed for the essential function of auto-focusing in mobile devices.Firstly,we introduce an effective target detection method combining the 3-frame difference algorithm and Gauss mixture model,which is robust for complex and changing background.Secondly,a stable tracking method is proposed using the local binary patter feature and camshift tracker.Auto-focusing is achieved by using the coordinate obtained during the detection and tracking procedure.Experiments show that the proposed method can deal with complex and changing background.When there exist multiple moving objects,the proposed method also has good detection and tracking performance.The proposed method implements high efficiency,which means it can be easily used in real mobile device systems.展开更多
In video surveillance, there are many interference factors such as target changes, complex scenes, and target deformation in the moving object tracking. In order to resolve this issue, based on the comparative analysi...In video surveillance, there are many interference factors such as target changes, complex scenes, and target deformation in the moving object tracking. In order to resolve this issue, based on the comparative analysis of several common moving object detection methods, a moving object detection and recognition algorithm combined frame difference with background subtraction is presented in this paper. In the algorithm, we first calculate the average of the values of the gray of the continuous multi-frame image in the dynamic image, and then get background image obtained by the statistical average of the continuous image sequence, that is, the continuous interception of the N-frame images are summed, and find the average. In this case, weight of object information has been increasing, and also restrains the static background. Eventually the motion detection image contains both the target contour and more target information of the target contour point from the background image, so as to achieve separating the moving target from the image. The simulation results show the effectiveness of the proposed algorithm.展开更多
为解决动态背景下运动目标检测所得目标较为微弱且目标区域离散的问题,文中提出一种动态背景下的运动目标检测算法。首先利用SURF(Speeded Up Robust Features)算法提取图像中的特征点,通过双向匹配法去除误匹配的SURF特征点对,并将特...为解决动态背景下运动目标检测所得目标较为微弱且目标区域离散的问题,文中提出一种动态背景下的运动目标检测算法。首先利用SURF(Speeded Up Robust Features)算法提取图像中的特征点,通过双向匹配法去除误匹配的SURF特征点对,并将特征点分为前景点和背景点两部分;再利用背景点计算仿射变换矩阵,以提高仿射变换矩阵的准确性,完成背景运动的补偿,消除背景运动对目标检测的影响。然后对补偿后的图像采用帧差法和形态学操作,完成对目标的初步提取。最后利用颜色、位移和位置信息对目标进行归并处理,完成运动目标的检测。实验结果表明,文中算法能够准确检测出运动目标,并且所得目标较为明显且目标区域连续。说明文中算法准确率高且具有较强的鲁棒性。展开更多
基金supported by ZTE Industry-Academia-Research Cooperation Funds
文摘In this paper,a non-contact auto-focusing method is proposed for the essential function of auto-focusing in mobile devices.Firstly,we introduce an effective target detection method combining the 3-frame difference algorithm and Gauss mixture model,which is robust for complex and changing background.Secondly,a stable tracking method is proposed using the local binary patter feature and camshift tracker.Auto-focusing is achieved by using the coordinate obtained during the detection and tracking procedure.Experiments show that the proposed method can deal with complex and changing background.When there exist multiple moving objects,the proposed method also has good detection and tracking performance.The proposed method implements high efficiency,which means it can be easily used in real mobile device systems.
文摘In video surveillance, there are many interference factors such as target changes, complex scenes, and target deformation in the moving object tracking. In order to resolve this issue, based on the comparative analysis of several common moving object detection methods, a moving object detection and recognition algorithm combined frame difference with background subtraction is presented in this paper. In the algorithm, we first calculate the average of the values of the gray of the continuous multi-frame image in the dynamic image, and then get background image obtained by the statistical average of the continuous image sequence, that is, the continuous interception of the N-frame images are summed, and find the average. In this case, weight of object information has been increasing, and also restrains the static background. Eventually the motion detection image contains both the target contour and more target information of the target contour point from the background image, so as to achieve separating the moving target from the image. The simulation results show the effectiveness of the proposed algorithm.
文摘为解决动态背景下运动目标检测所得目标较为微弱且目标区域离散的问题,文中提出一种动态背景下的运动目标检测算法。首先利用SURF(Speeded Up Robust Features)算法提取图像中的特征点,通过双向匹配法去除误匹配的SURF特征点对,并将特征点分为前景点和背景点两部分;再利用背景点计算仿射变换矩阵,以提高仿射变换矩阵的准确性,完成背景运动的补偿,消除背景运动对目标检测的影响。然后对补偿后的图像采用帧差法和形态学操作,完成对目标的初步提取。最后利用颜色、位移和位置信息对目标进行归并处理,完成运动目标的检测。实验结果表明,文中算法能够准确检测出运动目标,并且所得目标较为明显且目标区域连续。说明文中算法准确率高且具有较强的鲁棒性。
