人数识别即是对一定区域内活动人数的监测计数,在人群控制、流量监管等方面有着重要应用。例如,在百货商场或者机场中,对排队人数或者服务区休息人数进行估计可以为提升服务质量做出贡献。目前,研究人员已提出了一些基于摄像头和可穿戴...人数识别即是对一定区域内活动人数的监测计数,在人群控制、流量监管等方面有着重要应用。例如,在百货商场或者机场中,对排队人数或者服务区休息人数进行估计可以为提升服务质量做出贡献。目前,研究人员已提出了一些基于摄像头和可穿戴设备的人数识别方法,但是这些方案均存在一些不足,例如摄像头只能提供可视范围内的监控,可穿戴设备需要被监控对象有意识地穿戴。也有一些学者利用雷达相关技术实现了穿墙式感知识别,但是这类系统设计复杂,应用成本较高,多用于军事领域。文中提出了一种基于WiFi信号的室内人数识别方案WiCount,其利用信道状态信息(Channel State Information,CSI)的幅值波动来刻画室内人数的变化,利用机器学习算法实现对人的计数。WiCount旨在进行更细粒度的室内人数识别,即人在室内任意位置时该方法均能准确识别人数。它根据室内人数与CSI幅值变化的关系,提取了有效的数学特征,减弱了相同人数在室内不同位置所产生的CSI幅值波动差异,然后通过训练3种分类器(SVM、KNN、BP神经网络)来识别监测区域内的人数。在实验室和会议室分别部署了验证系统,结果显示,在人数规模较小的情况下,所提方法的识别效果良好。其中,实验室环境下,不超过4人时,系统的识别率达90%;会议室环境下,不超过2人,在监测区域内任意位置活动时,系统的识别率可达89.58%。展开更多
A novel supervised dimensionality reduction algorithm, named discriminant embedding by sparse representation and nonparametric discriminant analysis(DESN), was proposed for face recognition. Within the framework of DE...A novel supervised dimensionality reduction algorithm, named discriminant embedding by sparse representation and nonparametric discriminant analysis(DESN), was proposed for face recognition. Within the framework of DESN, the sparse local scatter and multi-class nonparametric between-class scatter were exploited for within-class compactness and between-class separability description, respectively. These descriptions, inspired by sparse representation theory and nonparametric technique, are more discriminative in dealing with complex-distributed data. Furthermore, DESN seeks for the optimal projection matrix by simultaneously maximizing the nonparametric between-class scatter and minimizing the sparse local scatter. The use of Fisher discriminant analysis further boosts the discriminating power of DESN. The proposed DESN was applied to data visualization and face recognition tasks, and was tested extensively on the Wine, ORL, Yale and Extended Yale B databases. Experimental results show that DESN is helpful to visualize the structure of high-dimensional data sets, and the average face recognition rate of DESN is about 9.4%, higher than that of other algorithms.展开更多
文摘人数识别即是对一定区域内活动人数的监测计数,在人群控制、流量监管等方面有着重要应用。例如,在百货商场或者机场中,对排队人数或者服务区休息人数进行估计可以为提升服务质量做出贡献。目前,研究人员已提出了一些基于摄像头和可穿戴设备的人数识别方法,但是这些方案均存在一些不足,例如摄像头只能提供可视范围内的监控,可穿戴设备需要被监控对象有意识地穿戴。也有一些学者利用雷达相关技术实现了穿墙式感知识别,但是这类系统设计复杂,应用成本较高,多用于军事领域。文中提出了一种基于WiFi信号的室内人数识别方案WiCount,其利用信道状态信息(Channel State Information,CSI)的幅值波动来刻画室内人数的变化,利用机器学习算法实现对人的计数。WiCount旨在进行更细粒度的室内人数识别,即人在室内任意位置时该方法均能准确识别人数。它根据室内人数与CSI幅值变化的关系,提取了有效的数学特征,减弱了相同人数在室内不同位置所产生的CSI幅值波动差异,然后通过训练3种分类器(SVM、KNN、BP神经网络)来识别监测区域内的人数。在实验室和会议室分别部署了验证系统,结果显示,在人数规模较小的情况下,所提方法的识别效果良好。其中,实验室环境下,不超过4人时,系统的识别率达90%;会议室环境下,不超过2人,在监测区域内任意位置活动时,系统的识别率可达89.58%。
基金Project(40901216)supported by the National Natural Science Foundation of China
文摘A novel supervised dimensionality reduction algorithm, named discriminant embedding by sparse representation and nonparametric discriminant analysis(DESN), was proposed for face recognition. Within the framework of DESN, the sparse local scatter and multi-class nonparametric between-class scatter were exploited for within-class compactness and between-class separability description, respectively. These descriptions, inspired by sparse representation theory and nonparametric technique, are more discriminative in dealing with complex-distributed data. Furthermore, DESN seeks for the optimal projection matrix by simultaneously maximizing the nonparametric between-class scatter and minimizing the sparse local scatter. The use of Fisher discriminant analysis further boosts the discriminating power of DESN. The proposed DESN was applied to data visualization and face recognition tasks, and was tested extensively on the Wine, ORL, Yale and Extended Yale B databases. Experimental results show that DESN is helpful to visualize the structure of high-dimensional data sets, and the average face recognition rate of DESN is about 9.4%, higher than that of other algorithms.
