基于骨架特征的人数统计

针对视频监控中行人在运动中将出现部分或严重遮挡的问题,提出了一种基于人体骨架特征的人数统计算法。首先,利用形态学骨架提取算法提取初始人体骨架图;然后,剔除骨架孤立点和骨架伪分支,得到最优人体骨架特征;最后,通过分析骨架的人头区域特征,建立人头检测响应规则,检测行人人头个数实现人数统计。实验结果表明,该算法能够解决视频监控人物相互之间部分遮挡和严重遮挡问题,针对相对稀疏的场景该算法人数统计准确率为95%左右。     

基于骨架特征的人数统计

针对视频监控中行人在运动中将出现部分或严重遮挡的问题,提出了一种基于人体骨架特征的人数统计算法。首先,利用形态学骨架提取算法提取初始人体骨架图;然后,剔除骨架孤立点和骨架伪分支,得到最优人体骨架特征;最后,通过分析骨架的人头区域特征,建立人头检测响应规则,检测行人人头个数实现人数统计。实验结果表明,该算法能够解决视频监控人物相互之间部分遮挡和严重遮挡问题,针对相对稀疏的场景该算法人数统计准确率为95%左右。     

基于Adaboost和码本模型的手扶电梯出入口视频监控方法

针对传统视频监控方法无法对密集前景目标进行准确分割的问题,提出一种基于Adaboost和码本模型的多目标视频监控方法。首先,通过训练得到Adaboost人头分类器,利用码本算法为垂直拍摄的手扶电梯出入口图像建立背景模型,提取前景图像对其进行人头检测和跟踪;之后,剔除行人目标得到物件目标,对物件目标进行跟踪;最后,根据行人和物件的运动特征进行监控。对12段出入口视频序列的实验结果表明,监控方法能够准确稳定地跟踪行人和物件,完成逆行检测、客流统计、行人拥堵和物件滞留等监控任务,处理速度达到36帧/秒,目标跟踪准确率达到94%以上,行为监控准确率达到95.8%,满足智能视频监控系统鲁棒性、实时性和准确性的要求。     

嵌入式系统中实时行人检测方法

随着计算机技术的不断发展,智能自动化的人数检测系统不断产生.人数检测对于企业或机构的信息化管理至关重要.传统人数检测方法因为肢体遮挡以及光照变化导致准确率较低.提出了针对人头特征的垂直检测方法,该特征可以保证在人流密度大的情况下无法被遮挡.该方法首先提取前景图像的梯度方向直方图特征,并通过SVM检测人头目标,利用头部的颜色特征,在相邻帧中使用MeanShift算法跟踪人头目标.根据人头目标轨迹进行过线检测,算法在嵌入式系统上进行了应用与测试,实验表明算法有较好的实时性与准确率.     

基于高斯混合模型的视频图像人数统计算法研究

针对摄像机俯视拍摄场景的人数统计问题,提出一种运算效率高、误检率低的人数统计方法。以人头部位为检测对象,采用运动侦测、边缘检测方法获取人头轮廓,在此基础上采用高斯混合模型分别对人头轮廓目标点集和椭圆模型进行建模,通过最小化人头轮廓目标点集与椭圆模型的高斯混合模型之间的欧氏距离求解椭圆参数,统计满足椭圆形状的轮廓数量,再通过形状滤波得到人数统计结果。人数统计对比实验结果表明,新方法的误检率低,且运算效率高。     

基于SSD卷积神经网络的公交车下车人数统计

传统典型的公交车人数统计方法在准确率和速度方面存在一些不足,且提取目标特征的效果较差.本文提出了基于深度卷积神经网络的公交车人数统计系统解决人群计数问题.首先制作数据集,难点在于所有用于训练的数据集均是手工标注.并且公交车摄像头角度比以往文献覆盖更广区域.本文首先比较了多种不同的深度卷积神经网络模型对乘客进行全身检测的效果.综合考虑检测速率、准确率等方面,最终采用单次检测器深度卷积神经网络模型对乘客进行人头目标检测,在线实时目标追踪算法实现人头的多目标追踪,跨区域人群计数方法统计公交车下车人数.系统准确率达到78.38%,运行速率约为每秒识别19.79帧.实现了人群计数.     

MB-LBP特征提取和粒子滤波相结合的运动目标检测与跟踪算法研究

在复杂环境下,由于行人密度大以及运动随机性,导致运动目标(行人)难以检测和跟踪,造成人员计数误差。提出一种MB-LBP(Multi-scale Block Local Binary Pattern)特征提取和粒子滤波相结合的运动目标检测与跟踪算法来解决此问题。该算法首先用AdaBoost提取MB-LBP特征训练生成分类器进行人头检测,并根据人头目标尺寸变化范围去除部分误检,然后用改进的粒子滤波算法预测跟踪多个运动目标,最后对跟踪的运动目标进行计数。实验结果表明,提出的算法能够对复杂环境下多个运动目标进行有效检测及跟踪,准确、快速地对视频帧中的人员进行计数。     

基于热点区域定义的人数统计方法研究

行人统计在智能监控领域中具有重要意义,但复杂背景环境以及行人运动过程中出现的遮挡现象导致当前方法的准确率并不高。此外,传统过线统计人数的方式的实际适用范围有限。考虑到现有方法的不足,提出了一种基于热点区域定义的人数统计方法。首先,利用自适应学习率背景建模提取运动目标前景,得到前景区域的位置和大小,扫描计算运动目标前景范围内的HOG特征,并判别是否存在头肩目标;然后,利用基于KCF的目标匹配算法跟踪头肩目标;最后,结合目标运动轨迹与提出的区域人数统计算法进行行人人数统计。采用 24fps的手机拍摄 的长度为10min、分辨率为960×720像素 的视频做人数统计实验。实验结果表明,所提算法在统计人数时正确率可达到93.1%,能满足实时性要求。该方法结合了检测效率和准确率,在背景环境复杂的场景下具有良好的效果,能适应各类人数统计的实际应用场景。     

实时人数计数系统

描述一个实时在线人数计数系统,该系统采用检测加跟踪的方法来实现人数计数功能。在检测阶段,采用MBLBP（multi-scale block LBP）特征,从运动区域上检测出行人。该特征速度快,并且在归一化下,能够适应多尺度的应用;在跟踪阶段,通过一个概率模型,将对行人的跟踪转化为对特征点的跟踪,并且在将检测目标和跟踪目标进行一一对应时,进一步利用各个目标内的特征点来完成相应的操作。最后用实际中不同场景下的视频,对系统的性能进行测试,同时还在一段公开的视频上进行了测试,实验结果表明,该系统能够在不同场景下较准确地实现人数计数功能。     

基于多特征融合的前向车辆检测方法

针对传统车辆检测方法定位精度不高的问题,提出一种基于多特征融合的前向车辆检测方法。采用基于直方图分析和自适应双阈值的方法分别实现阴影和边缘特征的准确分割,并通过阴影和边缘特征的综合分析,生成车辆假设区域。利用对称性、纹理和轮廓匹配度3个特征融合得到的综合特征对获得的车辆假设区域进行验证,剔除其中的误检区域。实验结果证明,该方法能在不同光照条件下自适应地进行车辆检测,检测率可达92%以上,且在检测率和误检率2项指标上均优于传统基于学习的方法。     

Tracking in a Dense Crowd Using Multiple Cameras

Tracking people in a dense crowd is a challenging problem for a single camera tracker due to occlusions and extensive motion that make human segmentation difficult. In this paper we suggest a method for simultaneously tracking all the people in a densely crowded scene using a set of cameras with overlapping fields of view. To overcome occlusions, the cameras are placed at a high elevation and only people’s heads are tracked. Head detection is still difficult since each foreground region may consist of multiple subjects. By combining data from several views, height information is extracted and used for head segmentation. The head tops, which are regarded as 2D patches at various heights, are detected by applying intensity correlation to aligned frames from the different cameras. The detected head tops are then tracked using common assumptions on motion direction and velocity. The method was tested on sequences in indoor and outdoor environments under challenging illumination conditions. It was successful in tracking up to 21 people walking in a small area (2.5 people per m²), in spite of severe and persistent occlusions.     

汉语语句核心动词的自动获取研究

语句核心动词的自动获取是以动词为中心的汉语语句分析中的重要组成部分。依据概念层次网络理论,在字词概念符号的基础上获取候选动词集合,根据动词的上下文语言环境对动词进行排除和分类处理,对可能作为语句核心动词的动词集合按照作语句核心结构的可能性大小排队,并验证其正确性。实验结果表明,在从真实语料切分得到的3121个语句中,经过排队处理后前三个动词作为语句核心动词的正确率达到了83%。错误分析表明进一步完善知识库及排除排队规则,还可以提高自动获取语句核心动词的正确率。     

Multiple camera people detection and tracking using support integration

This paper proposes a method to locate and track people by combining evidence from multiple cameras using the homography constraint. The proposed method use foreground pixels from simple background subtraction to compute evidence of the location of people on a reference ground plane. The algorithm computes the amount of support that basically corresponds to the “foreground mass” above each pixel. Therefore, pixels that correspond to ground points have more support. The support is normalized to compensate for perspective effects and accumulated on the reference plane for all camera views. The detection of people on the reference plane becomes a search for regions of local maxima in the accumulator. Many false positives are filtered by checking the visibility consistency of the detected candidates against all camera views. The remaining candidates are tracked using Kalman filters and appearance models. Experimental results using challenging data from PETS’06 show good performance of the method in the presence of severe occlusion. Ground truth data also confirms the robustness of the method.     

Robust people detection using depth information from an overhead Time-of-Flight camera

In this paper we describe a system for the automatic detection of multiple people in a scene, by only using depth information provided by a Time of Flight (ToF) camera placed in overhead position. The main contribution of this work lies in the proposal of a methodology for determining the Regions of Interest (ROI’s) and feature extraction, which result in a robust discrimination between people with or without accessories and objects (either static or dynamic), even when people and objects are close together. Since only depth information is used, the developed system guarantees users’ privacy. The designed algorithm includes two stages: an online stage, and an offline one. In the offline stage, a new depth image dataset has been recorded and labeled, and the labeled images have been used to train a classifier. The online stage is based on robustly detecting local maximums in the depth image (which are candidates to correspond to the head of the people present in the scene), from which a carefully ROI is defined around each of them. For each ROI, a feature vector is extracted, providing information on the top view of people and objects, including information related to the expected overhead morphology of the head and shoulders. The online stage also includes a pre-filtering process, in order to reduce noise in the depth images. Finally, there is a classification process based on Principal Components Analysis (PCA). The online stage works in real time at an average of 150 fps. In order to evaluate the proposal, a wide experimental validation has been carried out, including different number of people simultaneously present in the scene, as well as people with different heights, complexions, and accessories. The obtained results are very satisfactory, with a 3.1% average error rate.     

Development of head detection and tracking systems for visual surveillance

This paper proposes a technique for the detection of head nod and shake gestures based on eye tracking and head motion decision. The eye tracking step is divided into face detection and eye location. Here, we apply a motion segmentation algorithm that examines differences in moving people’s faces. This system utilizes a Hidden Markov Model-based head detection module that carries out complete detection in the input images, followed by the eye tracking module that refines the search based on a candidate list provided by the preprocessing module. The novelty of this paper is derived from differences in real-time input images, preprocessing to remove noises (morphological operators and so on), detecting edge lines and restoration, finding the face area, and cutting the head candidate. Moreover, we adopt a K-means algorithm for finding the head region. Real-time eye tracking extracts the location of eyes from the detected face region and is performed at close to a pair of eyes. After eye tracking, the coordinates of the detected eyes are transformed into a normalized vector of x-coordinate and y-coordinate. Head nod and shake detector uses three hidden Markov models (HMMs). HMM representation of the head detection can estimate the underlying HMM states from a sequence of face images. Head nod and shake can be detected by three HMMs that are adapted by a directional vector. The directional vector represents the direction of the head movement. The vector is HMMs for determining neutral as well as head nod and shake. These techniques are implemented on images, and notable success is notified.     

视频中多线索的人脸特征检测与跟踪

针对目前的人脸特征检测与跟踪算法存在的对环境适应能力差、缺乏自我检错能力的缺点，该文提出了一种多线索综合的新方法，多线索中包括基于深度信息的人脸区域粗分割，基于多关联模板匹配的人脸检测，利用多尺度Sobel卷积的特征提取，基于“特征眼”的人眼验证以及基于多视图的校验方法，多种线索互相补充，自我检错和纠错，对背景，光照及姿态变化具有较强的适应能力，实验表明该方法是有效的，鲁棒的。     

基于曲率尺度空间的人头检测方法研究

利用人头部轮廓的形状特征,提出一种基于曲率尺度空间的人头部检测算法。算法通过比较分析得出了人头部轮廓区别于其他部分轮廓的形状特征,在多曲率尺度下,计算物体轮廓曲线上每一个点的曲率,结合形状特征信息进行人头部检测。实验结果表明,算法有效解决了复杂背景下的人头检测问题,为人头部检测提供了新的途径。     

People-flow counting in complex environments by combining depth and color information

People-flow counting is one of the key techniques of intelligence video surveillance systems and the information of people-flow obtained from this technique is an very important evidence for many applications, such as business analysis, staff planning, security, etc. Traditionally, the color image information based methods encounter kinds of challenges, such as shadows, illumination changing, cloth color, etc., while the depth information based methods suffer from lack of texture. In this paper, we propose an effective approach of people-flow counting by combining color and depth information. First, we adopt a background subtraction technique to fast obtain the moving regions on depth images. Second, the water filling algorithm is used to effectively detect head candidates on the moving regions. Then we use the SVM to recognize the real heads from the candidates. Finally, we adopt a weighted K Nearest Neighbor based multi-target tracking method to track each confirmed head and count the people through the surveillance region. Four datasets constructed from two surveillance scenes are used to evaluate the proposed method. Experimental results show that our method outperform the state-of-the-art methods. Our method can work stably on condition of kinds of interruptions and can not only obtain high precisions, but also high recalls on four datasets.     

基于计算机视觉的人流量双向统计

提出了一种采用视频监控系统对人行通道口进行双向人流量计数的方法。首先建立发色模型与头部形状模型,采用形态学运算提取人的头部目标,然后跟踪目标建立人头目标移动链,依据目标链位置信息判别行人的进出方向,最后设置感兴趣的检测区域,并对通过该检测区域的行人计数。实验结果表明,该方法能实时有效地统计通道口处双向人流量。     

Exact and heuristic algorithms for balancing transfer lines when a set of available spindle heads is given

A balancing problem for paced tandem transfer lines with several spindle heads at each station is considered. A spindle head executes a block of operations. The set of all available spindle heads as well as the operations executed by each spindle head, the spindle head times and costs are known. There are operations with several spindle head candidates. The problem at the line design stage consists in the choice of spindle heads from the given set and their assignment to workstations. The goal is to minimize the line cost while satisfying the precedence, inclusion and exclusion constraints. An exact algorithm based on a mixed integer programming approach is developed. Two types of new heuristic algorithms are also suggested. One of them step‐by‐step assigns randomly spindle heads to a current workstation. The second uses depth‐first search techniques. Experimental results are reported.