排序方式: 共有67条查询结果,搜索用时 15 毫秒
1.
基于块的Mean-shift跟踪算法 总被引:1,自引:1,他引:0
针对传统Mean-shift跟踪算法在目标发生遮挡和形态变化时跟踪性能下降的缺点,提出了一种基于块的Mean-shift跟踪算法,该算法主要特点有:(1)将跟踪目标平均分块,每小块独立进行传统Mean-shift跟踪,利用小块跟踪未被遮挡的目标部分;(2)跟踪检测器检测目标小块跟踪的有效性,筛选出无效跟踪的目标小块,解决了目标分块造成跟踪性能下降的问题;(3)归一化互相关检测器和邻域一致检测增加了对目标空间信息的检测,弥补了Mean-shift算法的局限性,增加了跟踪的鲁棒性。实验表明,该算法在目标发生遮挡和形态变化时仍然可以有效的实现跟踪。 相似文献
2.
Experiment and analysis on microscopic characteristics of pedestrian movement in building bottleneck
TIAN Wei SONG WeiGuo * Lü Wei & FANG ZhiMing State Key Laboratory of Fire Science University of Science Technology of China Hefei China 《中国科学:信息科学(英文版)》2011,(7)
In this paper, evacuation experiments are carried out to study pedestrian movement behaviors in building bottleneck. An image processing method based on mean-shift algorithm is used to extract pedestrian movement trajectory. Based on the extracted trajectory, we analyze the microscopic movement characteristics of pedestrians such as lane formation, change of velocity and distance between two sequential pedestrians. A pedestrian lane is a group of pedestrians moving in a column. The lane formation is verifie... 相似文献
3.
基于颜色纹理特征的均值漂移目标跟踪算法 总被引:3,自引:0,他引:3
针对经典均值漂移跟踪算法采用单一的颜色特征对目标进行跟踪检测存在的不足,提出一种将纹理特征与颜色特征相结合的改进均值漂移目标跟踪算法.该算法首次提出特征联合相似度的概念,通过均值漂移算法联合相似度的最大化计算,正确快速地获取新一帧图像跟踪目标的位置.实验结果表明,该算法具有更高的可靠性,同时满足一般目标跟踪任务的实时性要求. 相似文献
4.
介绍了Android操作系统的构成,根据机器视觉中物体跟踪的具体要求,设计了一种基于Android平台的物体跟踪系统。该系统通过摄像头采集图像,在屏幕上选择要跟踪的物体,系统会自动计算目标中心,绘制目标轮廓。并从Opencv视觉库函数的选择,Android开发平台的搭建,模块功能的实现三个方面具体分析了系统的实现,该系统易于操作,实时性好,便于携带。 相似文献
5.
针对传统的视频人脸识别技术受限于理想的环境条件,无法应用于监控场景的弊端,提出了一种基于PTZ(Pan-Tilt-Zoom,旋转-俯仰-缩放)摄像头的人脸识别方法,并结合AdaBoost人脸检测以及Mean-shift跟踪算法进行了识别。实验结果表明,该方法克服了监控场景图像分辨率低的问题,具有较好的鲁棒性。 相似文献
6.
7.
为了实现对驾驶员人脸实时跟踪,提出了一种改进的Mean-shift算法。首先对人脸提取类Haar特征,使用类Haar特征构造弱分类器,然后根据样本的权值分布构造出强分类器,形成人脸检测分类器;由于光照变化等因素的影响,引入红外主动照明模式,通过隔离可见光照,基本上消除了光照变化对人脸检测造成的影响;针对Mean-shift算法在被跟踪目标发生快速移动时容易跟踪失败的缺点,改进了Mean-shift算法:当目标发生快速移动时,采用SSD(Sum of Square Dif-ference)算法进行全局搜索。以实际驾驶员人脸检测与跟踪实验为例进行了大量实验,提出的方法比Mean-shift算法的速度快、准确度高。 相似文献
8.
9.
Zhenqiu Zhang Gerasimos Potamianos Andrew W. Senior Thomas S. Huang 《Signal, Image and Video Processing》2007,1(2):163-178
The paper introduces a novel detection and tracking system that provides both frame-view and world-coordinate human location
information, based on video from multiple synchronized and calibrated cameras with overlapping fields of view. The system
is developed and evaluated for the specific scenario of a seminar lecturer presenting in front of an audience inside a “smart
room”, its aim being to track the lecturer’s head centroid in the three-dimensional (3D) space and also yield two-dimensional
(2D) face information in the available camera views. The proposed approach is primarily based on a statistical appearance
model of human faces by means of well-known AdaBoost-like face detectors, extended to address the head pose variation observed
in the smart room scenario of interest. The appearance module is complemented by two novel components and assisted by a simple
tracking drift detection mechanism. The first component of interest is the initialization module, which employs a spatio-temporal
dynamic programming approach with appropriate penalty functions to obtain optimal 3D location hypotheses. The second is an
adaptive subspace learning based 2D tracking scheme with a novel forgetting mechanism, introduced to reduce tracking drift
and increase robustness. System performance is benchmarked on an extensive database of realistic human interaction in the
lecture smart room scenario, collected as part of the European integrated project “CHIL”. The system consistently achieves
excellent tracking precision, with a 3D mean tracking error of less than 16 cm, and is demonstrated to outperform four alternative
tracking schemes. Furthermore, the proposed system performs relatively well in detecting frontal and near-frontal faces in
the available frame views.
This work was performed while Zhenqiu Zhang was on a summer internship with the Human Language Technology Department at the
IBM T.J. Watson Research Center. 相似文献
10.
Lei Shi 《Computational statistics & data analysis》2012,56(1):202-208
Deletion, replacement and mean-shift model are three approaches frequently used to detect influential observations and outliers. For general linear model with known covariance matrix, it is known that these three approaches lead to the same update formulae for the estimates of the regression coefficients. However if the covariance matrix is indexed by some unknown parameters which also need to be estimated, the situation is unclear. In this paper, we show under a common subclass of linear mixed models that the three approaches are no longer equivalent. For maximum likelihood estimation, replacement is equivalent to mean-shift model but both are not equivalent to case deletion. For restricted maximum likelihood estimation, mean-shift model is equivalent to case deletion but both are not equivalent to replacement. We also demonstrate with real data that misuse of replacement and mean-shift model in place of case deletion can lead to incorrect results. 相似文献