视轴偏角对三轴光电跟踪系统跟踪过程的影响

地平式、X-Y式光电跟踪系统存在空间跟踪盲区。三轴光电跟踪系统通过跟踪方式切换可以解决空间跟踪盲区问题,但视轴偏角使得脱靶量到轴转动量的转换不同于常规地平式跟踪系统,同时使系统具有不可见区域。分析和仿真表明:视轴偏角较小时,脱靶量与轴驱动量的转换可用常规地平式跟踪系统中的方法近似,否则,需用不同的计算方法;在三轴跟踪系统退化成的地平式跟踪系统的天顶跟踪盲区外即可进行跟踪方式切换。     

三轴光电跟踪系统的实时引导

三轴光电跟踪系统是地平式和X-Y式光电跟踪系统的有机组合,可以全方位跟踪空间运动目标,跟踪系统测量值与空间位置的多对一特性决定了跟踪策略的多样性和系统引导的复杂性,跟踪过程中对系统进行实时引导可以提高系统的跟踪精度及其可靠性。各种跟踪策略下三轴光电跟踪系统的引导过程包括:地平式的引导、X-Y式的引导、具视轴偏角的地平式引导、三轴光电跟踪系统的引导等。通过理论分析,给出了各种情况下的引导方法。     

基于球谐函数的三轴光电跟踪系统误差修正方法

三轴光电跟踪系统不存在空间跟踪盲区,为了提高系统引导或轨道测量精度,需要修正其系统误差。最小二乘系统误差修正方法精度较低,球谐函数系统误差修正有较高的修正精度,球谐函数系统误差修正方法应用到三轴光电跟踪系统中时存在困难。理论分析指出,应用球谐函数的系统误差修正需要在三轴光电跟踪系统中针对不同的方位建立多组修正参数。仿真表明,三轴光电跟踪系统中通过建立多组修正参数的球谐函数系统误差修正方法可以把系统误差降低到原来的30％。     

全自动双轴跟踪太阳能发电车设计

太阳能作为一种清洁无污染能源,虽然有着节能环保且经济的特点,但是使用率极低,该文就以设计的全自动双轴跟踪太阳能发电车为对象,对全自动双轴跟踪太阳能发电车从设计系统和应用范围等角度进行分析。     

车载光电跟踪设备高精度控制方法及仿真

介绍了保证车载光电跟踪设备高精度工作的一种数学方法,在光电跟踪设备基座上安放双轴倾角传感器,实时检测基座的水平度,通过传感器显示的数值,把基座的水平度变化量解算到光电跟踪设备的水平轴和垂直轴上去。构建所需的数学模型,分析工作原理,求出了光电跟踪设备水平轴和垂直轴所需转动角度的公式。建立CAD模型,并结合有虚拟样机技术对模型进行仿真分析。有限元分析表明：这种数学方法可以保证车载光电跟踪设备的工作精度。     

光电稳定跟踪平台跟踪控制回路性能测试系统

针对光电稳定跟踪平台跟踪性能测试困难的问题,依据光电产品跟踪系统的特点,设计一套光电平台跟踪控制回路性能测试系统。该系统中将运动物体放置在平行光管或者凹面反射镜的焦面处,通过控制物体的运动规律模拟无穷远处运动目标,实现在实验室内对跟踪角速度、跟踪角加速度等指标的测试。实验表明该系统结构简单,测试准确、方便,测试效率高。     

采用四象限探测器的双轴跟踪控制技术

采用四象限探测器作为位置传感器，设计出以DSP作为控制器的双轴跟踪控制试验演示系统。通过误差数据采样、计算和比较，在反馈作用下对误差修正，以快速扫描系统和大范围扫描系统构成的双轴联合机构实现运动目标的捕获跟踪。试验结果表明，该系统能够快速捕获并平稳跟踪合作目标，且调试方便，可靠性好，为下一步工程化奠定了基础。     

光电跟踪误差校准技术的研究

研制光电跟踪仪校准系统,利用激光跟踪仪跟踪光电跟踪仪并采集测量数据,剔除其中存在的粗大误差得到有效数据,采用球面拟合最小二乘法确定光电跟踪的中心。建立合理的坐标转换模型,实现激光跟踪仪的中心向光电跟踪仪的中心坐标转换,并将二者的测量数据进行对比得到校准结果。利用VB和MATLAB软件结合的方法对数据进行分析与处理,提升运算效率。举例给出光电跟踪仪校准系统的测试结果,并对其不确定度进行分析,证明了此校准系统的可靠性,为跟踪及瞄准系统的参数校准提供了参考。     

直流电动机在高精度光电跟踪系统中的应用

高精度光电跟踪系统用以完成对模拟动态目标的准确跟踪 ,要求跟踪元件响应快、动态性能好、精度高 ,因此跟踪元件的性能直接影响系统的跟踪特性和精度。直流力矩电机的优点是调速性能好 ,启动转矩大。正是这一特点 ,在高精度光电跟踪系统中尝试使用直流力矩电机控制反射镜跟踪目标。高精度光电跟踪系统要求在一个较大的视场内实现对静态目标和动态目标的高精度快速跟踪。     

视线跟踪技术研究

利用跟踪角膜反射-瞳孔中心的技术,构建了头部静止和头部自由运动两套视线跟踪系统,实时获取用户注视点的信息,通过电磁式位置跟踪器和共面彩色四点位置跟踪算法较好地解决了用户头部小范围运动问题,并且对系统的误差进行了详细分析.实验验证结果表明,头部静止视线跟踪系统实时跟踪误差小于1°,头部自由运动视线跟踪系统实时跟踪误差小于1cm,达到了良好的视线跟踪效果,为视线跟踪技术与人机交互设备的集成和广泛应用奠定了良好的基础.     

基于电感数字变换传感器的智能小车自动循迹系统

为了解决智能循迹小车常用的路径识别传感器易受电磁、光线等干扰的问题,提出了基于电感数字变换传感器的智能小车自动循迹系统。通过分析电感数字变换传感器的工作原理,得出通过扩展传感器感应线圈来扩展感应范围的方法,并基于该方法设计了一种使用单个LDC1000电感数字变换器的传感模块。该模块利用SN74LV4052A模拟开关实现对传感器线圈的扩展;采用MSP430单片机搭建了包括主控模块、电源模块、驱动模块和传感器模块的循迹硬件测试系统;通过实时采集和处理传感器的数据并对小车位置进行调整,实现了硬件测试系统循迹的功能;最后,在测试系统上进行了传感器对金属的感应实验和循迹测试。实验结果表明：该传感器可实时检测到轨道与线圈的水平和垂直距离的变化,可以为循迹功能的实现提供保证;该智能小车在5 mm宽的锡箔纸轨道上可以得到良好的循迹效果。研究结果表明该系统具有适用性强、检测技术稳定的特点,可以广泛应用于工业现场智能循迹。     

Automatic skin segmentation and tracking in sign language recognition

Skin segmentation and tracking play an important role in sign language recognition. A framework for segmenting and tracking skin objects from signing videos is described. It mainly consists of two parts: a skin colour model and a skin object tracking system. The skin colour model is first built based on the combination of support vector machine active learning and region segmentation. Then, the obtained skin colour model is integrated with the motion and position information to perform segmentation and tracking. The tracking system is able to predict occlusions among any of the skin objects using a Kalman filter (KF). Moreover, the skin colour model can be updated with the help of tracking to handle illumination variation. Experimental evaluations using real-world gesture videos and comparison with other existing algorithms demonstrate the effectiveness of the proposed work.     

基于最大后验概率密度的粒子过滤器跟踪算法

Kalman滤波的弱点是它无法解决非线性、非高斯问题的跟踪。为此提出了一种新型的跟踪算法,粒子过滤器算法。该算法采用加权的粒子集模型表示状态的分布,迭代跟踪状态的变化。其优点是它可以适应复杂环境的重叠和遮挡情况,且能同时跟踪多目标。采用最大后验概率模型确保了状态判断和估计的准确性。对重采样的分析减少了算法对噪声的敏感。并把样本安排在目标可能出现的区域。在眼睛跟踪系统上实现了该算法。仿真结果表明MAP模型在精度上与传统的方法比较提高7%。眼睛跟踪的结果证实了仿真的结果。     

快速运动目标的Mean shift跟踪算法

针对Mean shift本身的理论缺陷,提出Mean shift和卡尔曼滤波器相结合的快速目标跟踪算法。利用卡尔曼滤波器来获得每帧Mean shift算法的起始位置,然后再利用Mean shift算法得到跟踪位置。在目标出现大比例阻挡情况时,利用卡尔曼残差的计算来关闭和打开卡尔曼滤波器,此时,目标位置的线性预测替代了卡尔曼的作用。试验证明,本算法可以实现对快速运动目标的跟踪,对阻挡也有很好的鲁棒性。     

Zero-stopping constraint-based hybrid tracking model for dynamic and high-dense crowd videos

Owing to the importance of video surveillance in the public area, tracking finds significant applications using computer vision algorithms to observe the activity of human. In tracking, multi-object tracking is an active research to analyse and detect the activity of anomalies in the crowded scenes. Accordingly, different multi-object tracking algorithms are proposed in the literature to track the human behaviour of the crowded scenes. In this paper, we have presented a zero-stopping criteria-based hybrid tracking algorithm for high-dense crowd videos. Here, head objects are detected using the proposed objective function which considers both colour and texture property of videos. Then, tracking based on motion is performed using the proposed HSIM measure which includes structural similarity (SSIM) and the proposed similarity function. Along with, the data prediction model, exponential weighted moving average (EWMA), is also utilised to track the spatial location of human objects. These two tracking models are then hybridised to obtain the final tracked output. The experimentation is performed with three marathon sequences and the performance is evaluated with particle filtering-based algorithm using tracking number, tracking distance and optimal subpattern assignment metric (OSPA).     

A multimodal approach for individual tracking of people and their belongings

Abstract

In this study, a fully automatic surveillance system for indoor environments which is capable of tracking multiple objects using both visible and thermal band images is proposed. These two modalities are fused to track people and the objects they carry separately using their heat signatures and the owners of the belongings are determined. Fusion of complementary information from different modalities (for example, thermal images are not affected by shadows and there is no thermal reflection or halo effect in visible images) is shown to result in better object detection performance. We use adaptive background modeling and local intensity operation for object detection and the mean-shift tracking algorithm for fully automatic tracking. Trackers are refreshed to resolve potential problems which may occur due to the changes in object’s size, shape and to handle occlusion-split and to detect newly emerging objects as well as objects that leave the scene. The proposed scheme is applied to the abandoned object detection problem and the results are compared with the state of art methods. The results show that the proposed method facilitate individual tracking of objects for various applications, and provide lower false alarm rates compared to the state of art methods when applied to the abandoned object detection problem.     

The tracking study of 3D image feature points of aerobic movements of high difficulty

Tracking the 3D image feature points of aerobic movements of high difficulty can improve the quality of aerobic movements. The current method takes the windowed area as the tracking template to complete the track of feature points with low efficiency. For that reason, a method based on Gabor is presented with which to track the 3D image feature points of aerobic movements of high difficulty. The method extracts feature points with SIFT algorithms, classifies feature points according to AdaBoost algorithms, and tracks feature points through the image pyramid. The experiment shows that the mentioned method enhances the accuracy of tracking the 3D image feature points of aerobic movements.