共查询到18条相似文献,搜索用时 453 毫秒
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针对基于热释电红外传感器的人体跟踪系统在实际环境中,由于受环境噪声和硬件参数影响而误差较高的问题,本文提出了一种基于热释电红外传感器PIR Sensor(Pyroelectric Infrared Sensor)的高精度人体目标跟踪方案。该方案首先对检测区域内运动人体热辐射的红外信号特征进行了提取,然后利用PIR Sensor定位节点自身几何参数和探测数据,得到初步定位结果。最后通过Kalman滤波算法对初步定位结果进行滤波处理并更新目标的状态信息,实现对检测区域内人体目标的定位与跟踪。实验表明,该系统的跟踪误差与同类跟踪系统相比降低了71.96%,证明了该系统具有较高的跟踪精度。 相似文献
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针对热释电红外传感器(PIR)在人体探测领域中越来越广泛的应用,研究设计了一种基于PIR的检测定位系统,可实时完成对人员目标入侵探测区域时的检测与定位,并预推出人员目标的行进轨迹;该系统由多个PIR感知节点组成,每个感知节点通过传感在动、静两种状态下对探测区域进行信息采集;最终融合多节点与不同状态下传感器采集的数据,算出各个传感器的探测角度值,以交叉定位的算法,得到目标的定位坐标;经实验证明,该系统运行稳定,检测灵敏,定位效果很好,拓宽了热释电传感器在定位定向方面的使用范围。 相似文献
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为克服Prophet模型对残差自相关性考虑的缺失,时间推理能力的不足,提高被动红外(passive infrared,PIR)运动探测器检测结果的准确性,提出一种Prophet与SARIMA动态加权组合的预测模型.分析PIR运动探测器的特点,分析对比几种流行的预测算法的优劣,在此基础上设计Prophet-SARIMA的组合预测模型,统计和分析用户的行为.为获取最好的组合效果,设计动态加权组合算法,通过加权算法可确定最优的权值组合.通过实验验证了Prophet-SARIM A组合预测模型在人体红外数据的预测中具有更强的适用性和更高的准确性. 相似文献
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针对有向传感器网络(Directional Sensor Networks, DSNs)探测区域中PIR(Pyroelectric Infrared Sensor)传感器节点部署问题,设计了4种基于几何形状的节点部署方案,计算了各部署方案的节点密度。基于修改后的TIS测试编写仿真算法,在Matlab平台上对各节点部署方案进行了仿真实验,统计不同部署方案下的目标检测率,并对实验数据进行分析。结果表明,设计的4种部署方案的目标检测率均高于随机部署约10%;等腰三角形部署方案适用于节点数目充足的情况,能实现探测区域全覆盖,目标检测率可达80%以上;正方形部署方案适用于节点数目有限的情况,能实现探测区域大部分覆盖,目标检测率可达75%以上。 相似文献
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鉴于现有的火灾检测手段大多依赖于感温探测器和感烟探测器,但感温探测器和感烟探测器的探测具有一定的滞后性,无法实时准确地检测出初期火灾的问题,因此,构建了一个大规模多场景的火灾图像数据集;同时对图像数据集进行了火焰和烟雾目标标注,并提出了一种具有注意力机制的火灾检测算法,采用颜色分析的方法检测出图像中火焰和烟雾的疑似区域;再对火焰和烟雾目标的疑似区域进行关注,通过结合深度网络的特征提取能力,得到火灾目标的检测模型;实验结果表明,此方法在检测火灾任务上取得了更优的效果,相比于基于YOLOv3的火灾检测模型,mAP(mean average precision)提高了5.9%,同时满足了实时检测的需求。 相似文献
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为了增大热释电型红外传感器的探测范围,提高热释电传感器的探测灵敏度,提出了一种可实现大范围目标检测并且具有高灵敏性的热释电传感器的新型使用方法;该方法使热释电传感器在步进电机的带动下匀速转动,在周围环境基本不变的情况下,记录下其波形作为初始波形,在没有目标进入探测范围的情况下,波形将保持一定的规律基本不变;一旦目标进入传感器的探测范围,将打破原始波形规律,传感器即输出报警信号。由于热释电传感器是匀速转动的,所以一个热释电传感器就能对360°范围进行检测,大大扩大了热释电传感器的探测范围,并且当目标进入探测区域后即使静止不动依然会被动态下的传感器探测出来,解决了热释电传感器不能探测静止目标的问题,大大提高了热释电的灵敏度;该方法进一步拓展了热释电传感器的使用范围,进一步提升了热释电传感器在安防和智能家居系统中的应用。 相似文献
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The goal of Ambient Assisted Living (AAL) research is to improve the quality of life of the elderly and handicapped people and help them maintain an independent lifestyle with the use of sensors, signal processing and telecommunications infrastructure. Unusual human activity detection such as fall detection has important applications. In this paper, a fall detection algorithm for a low cost AAL system using vibration and passive infrared (PIR) sensors is proposed. The single-tree complex wavelet transform (ST-CWT) is used for feature extraction from vibration sensor signal. The proposed feature extraction scheme is compared to discrete Fourier transform and mel-frequency cepstrum coefficients based feature extraction methods. Vibration signal features are classified into “fall” and “ordinary activity” classes using Euclidean distance, Mahalanobis distance, and support vector machine (SVM) classifiers, and they are compared to each other. The PIR sensor is used for the detection of a moving person in a region of interest. The proposed system works in real-time on a standard personal computer. 相似文献
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提出了一种可以在嵌入式平台上实时运行的驾驶员状态检测算法. 状态检测采用了基于统计学习的Adaboost算法与动态建模算法. 与传统的采用主动红外光的方法相比, 本系统采用对人眼更为安全的被动式方法, 且对光线的变化有更好的鲁棒性. 算法的主要创新点是: 1) 提出了检测区域自适应调整的单双眼检测相结合的Adaboost人眼检测算法, 提高了人眼检测的准确性与速度; 2)提出基于高斯混合模型的人眼动态建模跟踪算法, 自动提取驾驶员眼睛区域灰度分布的信息, 实现了对不同驾驶员人眼的建模与跟踪定位. 在多个公共数据集以及实车采集的视频上进行的实验表明, 该算法能够准确判断驾驶员的状态, 满足实时处理的要求. 相似文献
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Soheil Bahrampour Asok Ray Soumalya Sarkar Thyagaraju Damarla Nasser M. Nasrabadi 《Pattern recognition letters》2013,34(16):2126-2134
This paper addresses the problem of target detection and classification, where the performance is often limited due to high rates of false alarm and classification error, possibly because of inadequacies in the underlying algorithms of feature extraction from sensory data and subsequent pattern classification. In this paper, a recently reported feature extraction algorithm, symbolic dynamic filtering (SDF), is investigated for target detection and classification by using unmanned ground sensors (UGS). In SDF, sensor time series data are first symbolized to construct probabilistic finite state automata (PFSA) that, in turn, generate low-dimensional feature vectors. In this paper, the performance of SDF is compared with that of two commonly used feature extractors, namely Cepstrum and principal component analysis (PCA), for target detection and classification. Three different pattern classifiers have been employed to compare the performance of the three feature extractors for target detection and human/animal classification by UGS systems based on two sets of field data that consist of passive infrared (PIR) and seismic sensors. The results show consistently superior performance of SDF-based feature extraction over Cepstrum-based and PCA-based feature extraction in terms of successful detection, false alarm, and misclassification rates. 相似文献
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Location estimation or localization is one of the key components in IoT applications such as remote health monitoring and smart homes. Amongst device-free localization technologies, passive infrared (PIR) sensors are one of the promising options due to their low cost, low energy consumption, and good accuracy. However, most of the existing systems are complexly designed and difficult to deploy in real life, in addition, there is no public dataset available for researchers to benchmark their proposed localization and tracking methods. In this paper, we propose a system and a dataset collected from our PIR system consisting of commercial-of-the-shelf (COTS) sensors without any modification. Our dataset includes profile data of 36 classes that have over 1,000 samples of different walking directions and test data consisting of multiple scenarios with a sequence length of over 2,000 timesteps. To evaluate our system and dataset, we implement various deep learning methods such as CNN, RNN, and CNN–RNN. Our results prove the applicability and feasibility of our system and illustrate the viability of deep learning methods for PIR-based localization and tracking. We also show that our dataset can be converted for coordinate estimation so that deep learning methods and particle filter approaches can be applied to estimate coordinates. As a result, the best performer achieves a distance error of 0.25 m. 相似文献