Human tracking system based on adaptive multi-feature mean-shift for robot under the double-layer locating mechanism

Human tracking has been a challenging task for robot in the past decades. In this paper, to realize the human following in a cluttered environment, a human tracking system based on adaptive multi-feature mean-shift (AMF-MS) under the double-layer locating mechanism (DLLM) is proposed to solve the problem of distinguishing target, occlusion, and quick turning. The DLLM, considering the course location processing and fine location processing, is designed to estimate the person’s position using the fusion of heterogeneous data. As an ID tag attached on target can be detected by RF antennas, the course locating method can track the target easily and quickly. The Bayes rule is introduced to calculate the probability where the tag exists due to the instability of RF signals. In the fine locating step, the AMF-MS is proposed because it can reduce computational load and represent target by multi-feature histogram function. Meanwhile, we combine extended Kalman filter and AMF-MS to overcome MS’s inability of occlusion. To control the robot following the target person precisely, an intelligent gear shift strategy based on fuzzy control is implemented by analyzing the robot structure. Experiments demonstrate that the proposed approach is robust to handle complex tracking conditions, and show the system has an optimum performance.     

改进的快速自适应分块均值漂移算法

为了实现复杂情况下的目标跟踪,该文提出了改进的自适应分块策略。通过主分量分析的方法确定不同时刻帧的矩形框大小,然后在不同的情况下根据一定的条件,决定不同的分块方法,从而提高目标在旋转、遮挡、缩放等复杂环境下的跟踪成功率。同时,为了减少寻找目标的时间,该文还提出两帧法,即通过前两帧计算出的质心象素点的个数差,快速预测并确定当前帧中目标可能的质心位置。实验表明,这两种方法相结合不仅有效提高了跟踪的成功率,而且减少了迭代次数,提高了算法的跟踪效率。     

一种改进的粒子滤波跟踪算法

传统粒子滤波跟踪算法的退化现象和巨大的计算量不利于其应用,尤其在实时性要求较高的视频监控场合。引入均值漂移算法进行粒子的采样调整,采用积分直方图加快每个粒子的直方图计算速度,以改进传统粒子滤波跟踪算法的速度和跟踪效果,满足实时跟踪需要。实验结果证明了改进算法的有效性。     

基于漏磁检测的钢管探伤监控系统设计

针对钢管漏磁检测的特殊性,设计开发了一种带有干扰滤波的探伤监控系统,采用信号接收模块配合多串口卡,开辟了多个内存缓冲区,将数据进行多线程并行分析处理,极大地提高了数据处理的速度和检测的实时性。采用均值平移小波阈值去噪法对漏磁检测信号进行降噪处理,有效降低了测量噪音等干扰因素的影响,并消除伪吉布斯现象。软件部分利用 Visual Studio C＃进行开发,可以实现无人值守条件下的全自动流水线检测控制、自动钢管缺陷判断、报警和分析处理等功能。系统具有高的实用性和可靠性。     

H.264压缩域中mean-shift聚类运动目标分割算法

针对视频监控、检索,提出了一 种在H.264压缩域下运动对象分 割的新算法。根据实际监控应用特点,算法主要利用H.264码流中提 取的运动矢量(MV)、分块尺寸信息对原始的MV场(MVF)进 行可靠性分析以及空间滤波、mean-shift聚类处理,从而得到可信度较高的 MVF场。首先,从H.264码流中获取原 始MV 并进行归一化处理,同时对原始MVF进行两步空间滤波;其次,对不同的块大 小分配相应的权值作为每个样本 的权重系数,将处理之后的MVF作为样本空间,利用mean-shift聚类获取 真实的MVF;最后,根据可靠的MVF 标记运动目标。实验结果表明,本文提出的mean-shift聚类运动目标分割算 法可以获得有效并可靠的分割结果。     

Design and implementation of a real-time eye tracking system

Driver fatigue severely affects driver's alertness and ability to drive safely. There are vital problems related to drivers fatigue on driving of trains, vehicles and airplanes. Therefore, the driver fatigue research is important. In this paper, we first study the impact of eye locations on face recognition accuracy, with Haar-like feature and AdaBoost classifier, face and eye area can be detected quickly and accurately. In the part of eye tracking, cam-shift based mean-shift algorithm is used to track the eyes. This method could automatically adjust the size of tracking window according to the different posture of driver. The performance of our eye detection method is validated by using image database with more than 6000 pictures. In addition, our real-time eye tracking system has been tested on railway line segment (China). There are 5 train drivers involved in the experiment. The validation shows that our eye detector has an overall 93% eye detection rate.     

基于Mean-shift的多目标跟踪算法的设计

针对视频序列中目标的跟踪问题,提出了一种基于模版匹配的多目标跟踪算法.该算法提取目标模版灰度分布特征,并在目标匹配过程中采用Bhattacharyya系数在图像中寻找相似性最高的区域.仿真结果表明,用该算法进行多目标跟踪时,跟踪性能远优于其它特征匹配算法和多模型算法,而且计算量小,能保证跟踪的实时性.     

基于目标中心距离加权和图像特征识别的跟踪算法

本文提出了一种实时跟踪算法,以目标中心距离加权的灰度图像作为模板,采用mean-shift迭代方法实现目标的空间定位;同时建立二值图像模板,根据目标的图像特征,进行尺度定位.本算法改进了kernel-based算法对变尺度目标的跟踪.对于超过一定像素的较大目标,本算法进行"降采样",大大减小运算量,从而实现了对大尺度目标的实时跟踪.实时视频流的跟踪实验验证了该方法的有效性.     

基于多判据的散乱点云特征点提取算法

为了有效获取散乱点云中的尖锐特征点和边界特征点,提出一种利用多判据融合的特征点提取算法。首先利用一种改进的k-d tree构建点云拓扑,搜索样点的K局部邻域;然后利用法向夹角判定准则、核密度判定准则、场力和判定准则分别求取各个样点局部邻域的三个特征参数,最后通过加权计算特征参数得到每个样点的特征值与全局判定阈值,特征值比阈值大的点即为特征点。实验证明,该算法能有效的获取散乱点云中边沿特征点与尖锐特征点。     

基于Mean-shift和DoG的卡通化图像生成算法

从数字图像直接生成卡通化效果有着重要的应用,文中提出一种新的图像卡通化效果生成算法,首先利用均值漂移算法去除图像中颜色细节信息和纹理细节信息,用大色块替代原图像中属性相同的区域,然后对均值漂移滤波后的图像利用高斯差分算子提取边缘信息,最后将均值滤波图像和边缘图像进行融合获得卡通化效果。实验结果表明,利用本算法获得很好的卡通化效果图像。