空间目标天基成像探测信噪比分析

基于光散射理论和轨道动力学,引入区域分解、网格划分和矢量坐标变换的思想,利用双向反射分布函数建立了空间目标等效星等的数学模型。结合空间目标天基成像探测的噪声来源建立了空间目标探测信噪比的数学模型。以空间目标天基红外系统为例,根据目标的结构特性、材料特性、背景特性、轨道特性及探测器的轨道特性、性能参数等,计算了目标等效星等和目标探测信噪比随时间的变化关系。结果表明,目标等效星等在观测时间内存在约3个星等的差别,导致目标探测信噪比在观测过程中也随之变化。仿真结果验证了建模方法的正确性,实现了空间目标天基成像探测信噪比的动态分析。     

基于DM8148的警戒区域告警算法的实现

智能视频监控作为安全防范的一种有效手段,有广阔的应用前景。针对这一需求,提出了一种基于DM8148平台的警戒区域告警算法。该算法采用混合高斯模型,结合背景减法检测移动目标,线性预测结合Blob匹配跟踪运动轨迹,分析目标行为判断是否存在警戒区域入侵现象。通过实验验证,该算法所具备的低误报率和高报警精度能满足智能监控对高效率、高实时性要求。     

基于局部特征描述的目标定位

针对SURF不能满足实时性、BRISK匹配率低的特点,本文采用SURF检测关键点与BRISK计算描述子相结合的方法,提出一种新的特征描述方法SURF-BRISK。本文首先通过SURF-BRISK描述方法进行特征匹配,然后通过RANSAC去除误匹配,最后通过正确的匹配点对计算仿射变换的6个参数并进行目标定位。实验表明, SURF-BRISK特征描述方法不仅具有实时性和鲁棒性,而且在目标定位中取得了较好的结果。     

改进特征和时间融合框架的区分式跟踪算法

文章基于多示例学习的跟踪框架,引入改进型的分布场特征并融合目标时间维度信息,提出了一种新的跟踪算法。新的特征能够更为有效地描述目标的空间结构信息,对于目标模糊、局部遮挡以及细微形变有良好的鲁棒性。加入的目标时间维度信息融合方法,包含了目标的历史信息,同时也能响应目标的外观变化,提高了跟踪器从跟踪异常中恢复的能力。通过对比新算法与其他先进算法在多组测试视频上的跟踪结果,可以发现本文提出的算法具有更为优异的性能,能够在各种复杂情况下对目标进行稳定的跟踪。     

Real‐Time License Plate Detection in High‐Resolution Videos Using Fastest Available Cascade Classifier and Core Patterns

We present a novel method for real‐time automatic license plate detection in high‐resolution videos. Although there have been extensive studies of license plate detection since the 1970s, the suggested approaches resulting from such studies have difficulties in processing high‐resolution imagery in real‐time. Herein, we propose a novel cascade structure, the fastest classifier available, by rejecting false positives most efficiently. Furthermore, we train the classifier using the core patterns of various types of license plates, improving both the computation load and the accuracy of license plate detection. To show its superiority, our approach is compared with other state‐of‐the‐art approaches. In addition, we collected 20,000 images including license plates from real traffic scenes for comprehensive experiments. The results show that our proposed approach significantly reduces the computational load in comparison to the other state‐of‐the‐art approaches, with comparable performance accuracy.     

光电跟踪系统中扩展目标的成像对比度分析

分析了影响大气层内扩展目标成像对比度的主要因素,研究了整体倾斜校正对扩展目标对比度影响的数学关系,并分析了采用绝对差分算法提取整体相位倾斜的复杂度优化问题。采用快速倾斜镜对成像光束整体倾斜进行校正能够提高扩展目标的成像对比度,降低后端数字图像处理的难度,进一步减少目标识别和跟踪所需要的时间,对提高低对比度目标的光电跟踪效率具有参考意义。     

NUMA结构的高效实时稳定的垃圾回收算法

针对非一致性内存访问架构(NUMA)在垃圾回收(GC)过程中存在大量的远程内存读写导致GC性能降低的问题,对GC过程的各个阶段进行分析与研究,提出了一种基于NUMA结构的高效实时稳定的GC算法。该算法首先基于NUMA结构改进传统分代GC机制的堆空间布局,然后通过控制GC过程中扫描活跃对象阶段的初始根对象选取、动态负载均衡阶段截取任务队列的选取以及复制活跃对象阶段对象复制位置的选取,大大减少GC过程中的远程访问次数。这种改进的GC机制对所有NUMA结构具有通用性。以Godson-3处理器的NUMA平台为例进行的实验结果显示,优化的GC机制极大地缩短了GC的时间,而且提高了应用程序的性能以及稳定性。在SPECjvm2008测试中,GC时间平均缩短了14.6%(GC总时间缩短4.1%~41.58%),应用程序的性能平均提升了4.68%(最高提升17.8%),应用程序的性能稳定性提升了76.2%。     

面向目标检测的稀疏表示方法研究进展

目标检测作为图像理解的一个基础而重要的课题深受国内外学者的重视,在军事和民用中具有广泛应用.应用背景的多样性和复杂性使得传统目标检测算法难以克服复杂背景、噪声干扰、光照变化以及非刚体形变、遮挡、弱特征、尺度、视角和姿态变化等因素的影响.近些年来发展起来的稀疏表示方法为图像处理及目标检测研究提供了新的思路,本文概述了稀疏表示基本概念和理论研究进展,综述了稀疏表示方法在目标特征学习、目标分类器和滤波器设计以及多源信息融合目标检测等目标检测领域中的国内外重要研究进展,并展望了稀疏表示方法在目标检测领域的发展方向.     

无人机影像目标检测技术研究进展

无人机影像目标检测技术是无人机影像智能化分析中的关键技术，开展无人机影像目标检测技术的研究有着广阔的应用前景和价值。介绍了无人机影像中目标检测技术的发展历程，简述了无人机影像目标检测技术在森林防火、农业信息采集、电力线路检测、路桥损害检测以及军事侦察等5种特定领域的应用情况，总结归纳了目标检测性能评价时常用的数据集和标准，并对未来无人机影像目标检测技术的发展态势进行了展望。     

A framework for cloned vehicle detection

Rampant cloned vehicle offenses have caused great damage to transportation management as well as public safety and even the world economy. It necessitates an efficient detection mechanism to identify the vehicles with fake license plates accurately, and further explore the motives through discerning the behaviors of cloned vehicles. The ubiquitous inspection spots that deployed in the city have been collecting moving information of passing vehicles, which opens up a new opportunity for cloned vehicle detection. Existing detection methods cannot detect the cloned vehicle effectively due to that they use the fixed speed threshold. In this paper, we propose a two-phase framework, called CVDF, to detect cloned vehicles and discriminate behavior patterns of vehicles that use the same plate number. In the detection phase, cloned vehicles are identified based on speed thresholds extracted from historical trajectory and behavior abnormality analysis within the local neighborhood. In the behavior analysis phase, consider the traces of vehicles that uses the same license plate will be mixed together, we aim to differentiate the trajectories through matching degreebased clustering and then extract frequent temporal behavior patterns. The experimental results on the real-world data show that CVDF framework has high detection precision and could reveal cloned vehicles’ behavior effectively. Our proposal provides a scientific basis for traffic management authority to solve the crime of cloned vehicle.