共查询到18条相似文献,搜索用时 109 毫秒
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随着公共活动在规模和数量上的不断增长,人群的密度估计已成为人群监控安全领域的重要问题,采用基于纹理特征的估计方法,并针对常规相机在人群监控领域的局限性,采用鱼眼镜头对人群进行监控,通过对鱼眼图像进行了预处理、 划分图像 ROI区域等操作,进而提取基于图像纹理的9维特征向量,再利用SVM分类器,解析人群图像的密度等级信息,通过对比实验验证,该算法人群密度分类准确率明显高于仅使用灰度共生矩阵参数的分类结果,可见,基于鱼眼视频图像的人群密度估计方法是有效的,且适用于人群安全监控领域。 相似文献
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针对人群密集公共场所的视频监控,传统的人工监控因为其局限性,已不能满足实际需要,人群智能监控应运而生,而人群密度成为监控的重要对象。基于像素点统计的人群密度估计方法简单直观,但仅适用于人群密度较低场合,密度较高时误差较大。对中高密度人群,本文给出了一种基于灰度共生矩阵和分形的人群特征提取方法,进而利用支持向量机实现人群密度分类。对基于视频的人群密度估计实验结果表明本文提出的方法是有效的。 相似文献
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基于时间窗的自适应核密度估计运动检测方法 总被引:2,自引:0,他引:2
在对非参数核密度估计算法改进的基础上,针对远程视频监控中存在前景检测不够精确、实时性低等问题,提出了用于自适应背景更新的基于像素时间信息窗的核密度估计(TIW-KDE)算法,该算法充分利用时间轴上的前景帧的信息,自适应地将背景划分为动态背景区域和非动态背景区域,对动态背景区域用改进的非参数核密度估计算法进行更新,对非动态背景区域采用渐进式算法更新,有效解决了非参数核密度估计算法在背景更新时引起的背景污染和计算量大问题。实验结果表明,该算法在提高前景检测精确性的前提下,在处理实时性方面得到很大提高。 相似文献
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针对DSP视频监控系统中,实时视频烟雾检测功能的需求,提出一种基于烟雾飘动性分析的快速视频烟雾检测算法.相比于现有相关算法,在完成对给定视频样本学习后,该算法无需存储视频的历史帧数据,也不需要学习场景背景,而直接对原始视频图像分块提取烟雾飘动特征,然后经Bayesian决策输出检测结论.结合提出的一组新颖的视频烟雾飘动特征,新算法同时获得了较低的计算复杂度和较高的检测性能.综合来看,新算法能更好地适用于实时视频监控系统中的烟雾检测需求. 相似文献
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提出了一种人群密度估计算法,将像素统计和纹理特征两种基本方法进行有效结合。前景提取使用改进的Vibe算法,设定感兴趣区域(ROI)来减少运算量。同时,引入形态学处理和透视矫正消除了因人物远近所造成的误差。并设定了一套人群密度等级划分的标准,克服了因人群密度高低频繁变化造成的误差。最终,实验结果显示运算速度和正确率均较为可观,证明了本算法的可靠性。 相似文献
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Recently significant progress has been made in the field of person detection and tracking. However, crowded scenes remain particularly challenging and can deeply affect the results due to overlapping detections and dynamic occlusions. In this paper, we present a method to enhance human detection and tracking in crowded scenes. It is based on introducing additional information about crowds and integrating it into the state-of-the-art detector. This additional information cue consists of modeling time-varying dynamics of the crowd density using local features as an observation of a probabilistic function. It also involves a feature tracking step which allows excluding feature points attached to the background. This process is favorable for the later density estimation since the influence of features irrelevant to the underlying crowd density is removed. Our proposed approach applies a scene-adaptive dynamic parametrization using this crowd density measure. It also includes a self-adaptive learning of the human aspect ratio and perceived height in order to reduce false positive detections. The resulting improved detections are subsequently used to boost the efficiency of the tracking in a tracking-by-detection framework. Our proposed approach for person detection is evaluated on videos from different datasets, and the results demonstrate the advantages of incorporating crowd density and geometrical constraints into the detection process. Also, its impact on tracking results have been experimentally validated showing good results. 相似文献
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对盖革模式APD激光雷达系统的距离像重构算法进行了研究,设计了一种基于像素邻域核密度估计的重构算法。从系统原理出发,结合探测概率模型研究了距离像重构算法的理论基础。根据系统特点提出了一种基于像素邻域核密度估计的改进算法,并对其原理进行了分析。通过仿真数据对直方图算法和邻域核密度估计算法进行了验证,以距离重构准确率曲线进行了定量评价对比,并进一步将算法应用到真实盖革模式APD激光雷达数据中进行了距离像重构实验。实验结果表明,在低帧数时,基于像素邻域统计核密度估计的重构算法可有效提高距离像重构的效果。 相似文献
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For reasons of public security, modeling large crowd distributions for counting or density estimation has attracted significant research interests in recent years. Existing crowd counting algorithms rely on predefined features and regression to estimate the crowd size. However, most of them are constrained by such limitations: (1) they can handle crowds with a few tens individuals, but for crowds of hundreds or thousands, they can only be used to estimate the crowd density rather than the crowd count; (2) they usually rely on temporal sequence in crowd videos which is not applicable to still images. Addressing these problems, in this paper, we investigate the use of a deep-learning approach to estimate the number of individuals presented in a mid-level or high-level crowd visible in a single image. Firstly, a ConvNet structure is used to extract crowd features. Then two supervisory signals, i.e., crowd count and crowd density, are employed to learn crowd features and estimate the specific counting. We test our approach on a dataset containing 107 crowd images with 45,000 annotated humans inside, and each with head counts ranging from 58 to 2201. The efficacy of the proposed approach is demonstrated in extensive experiments by quantifying the counting performance through multiple evaluation criteria. 相似文献
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Crowd counting with density estimation has been an active research community due to its significant applications in the fields of public security, video surveillance, traffic monitoring. However, Crowd counting for congested scenes often suffers from some obstacles including severe occlusions, large scale variations, noise interference, etc. In this paper, using the first ten layers of a modified VGG16 and dilated convolution layers as the framework, we have proposed a CNN based crowd counting and density estimation model improved by the attention aware modules with residual connections. To tackle the problem of noise interference, convolutional block attention modules have been introduced into the deep network to segment the foreground and background to focus on interest information, refining deeper features of the input image. To improve information transmission and reuse, residual connections are utilized to link 3 attention blocks. Meanwhile, dilated convolution layers keep larger reception fields and obtain high-resolution density maps. The proposed method has been evaluated on three public benchmarks, i.e. Shanghai Tech A & B, UCF-QNRF and MALL, achieving the mean absolute errors of 64.6 & 8.3, 113.8 and 1.68, respectively. The results outperform some existing excellent approaches. This indicates that the proposed model has high accuracy and better robustness, which is suitable for crowd counting and density estimation in various congested scenes. 相似文献
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针对在低信噪比目标检测问题中,基于PHD的粒子滤波检测前跟踪算法(PHD-TBD)存在目标位置估计误差较大的缺陷,提出一种结合粒子群优化算法的基于PHD的粒子滤波检测前跟踪方法(PSO-PHD-TBD)。该算法在滤波预测和更新步骤之间加入基于NSGA-Ⅱ的多目标粒子群优化算法,结合量测信息将预测完成的粒子集的分布进行优化,将所有粒子转移到后验概率密度较大的区域,进而改善了多目标位置估计的性能;然后使用基于密度聚类的DBSCAN算法对粒子聚类,提取目标状态。仿真实验表明,在不同信噪比条件下,PSO-PHD-TBD在多目标数目估计情况与PHD-TBD算法一致,而位置估计精度明显优于PHD-TBD算法。 相似文献