A robust system for real-time pedestrian detection and tracking

A real-time pedestrian detection and tracking system using a single video camera was developed to monitor pedestrians. This system contained six modules: video flow capture, pre-processing, movement detection, shadow removal, tracking, and object classification. The Gaussian mixture model was utilized to extract the moving object from an image sequence segmented by the mean-shift technique in the pre-processing module. Shadow removal was used to alleviate the negative impact of the shadow to the detected objects. A model-free method was adopted to identify pedestrians. The maximum and minimum integration methods were developed to integrate multiple cues into the mean-shift algorithm and the initial tracking iteration with the competent integrated probability distribution map for object tracking. A simple but effective algorithm was proposed to handle full occlusion cases. The system was tested using real traffic videos from different sites. The results of the test confirm that the system is reliable and has an overall accuracy of over 85%.

Multiple Pedestrian Detection and Tracking in Night Vision Surveillance Systems

Pedestrian detection and tracking are vital elements of today’s surveillance systems,which make daily life safe for humans.Thus,human detection and visualization have become essential inventions in the field of computer vision.Hence,developing a surveillance system with multiple object recognition and tracking,especially in low light and night-time,is still challenging.Therefore,we propose a novel system based on machine learning and image processing to provide an efficient surveillance system for pedestrian detection and tracking at night.In particular,we propose a system that tackles a two-fold problem by detecting multiple pedestrians in infrared(IR)images using machine learning and tracking them using particle filters.Moreover,a random forest classifier is adopted for image segmentation to identify pedestrians in an image.The result of detection is investigated by particle filter to solve pedestrian tracking.Through the extensive experiment,our system shows 93%segmentation accuracy using a random forest algorithm that demonstrates high accuracy for background and roof classes.Moreover,the system achieved a detection accuracy of 90%usingmultiple templatematching techniques and 81%accuracy for pedestrian tracking.Furthermore,our system can identify that the detected object is a human.Hence,our system provided the best results compared to the state-ofart systems,which proves the effectiveness of the techniques used for image segmentation,classification,and tracking.The presented method is applicable for human detection/tracking,crowd analysis,and monitoring pedestrians in IR video surveillance.

A novel method for tracking pedestrians from real-time video

This novel method of Pedestrian Tracking using Support Vector (PTSV) proposed for a video surveillance instrument combines the Support Vector Machine (SVM) classifier into an optic-flow based tracker. The traditional method using optical flow tracks objects by minimizing an intensity difference function between successive frames, while PTSV tracks objects by maximizing the SVM classification score. As the SVM classifier for object and non-object is pre-trained, there is need only to classify an image block as object or non-ob-ject without having to compare the pixel region of the tracked object in the previous frame. To account for large motions between successive frames we build pyramids from the support vectors and use a coarse-to-fine scan in the classification stage. To accelerate the training of SVM, a Sequential Minimal Optimization Method (SMO) is adopted. The results of using a kernel-PTSV for pedestrian tracking from real time video are shown at the end. Comparative experimental results showed that PTSV improves the reliability of tracking compared to that of traditional tracking method using optical flow.

Tracking Pedestrians Under Occlusion in Parking Space

Many traffic accidents occur in parking lots.One of the serious safety risks is vehicle-pedestrian conflict.Moreover,with the increasing development of automatic driving and parking technology,parking safety has received significant attention from vehicle safety analysts.However,pedestrian protection in parking lots still faces many challenges.For example,the physical structure of a parking lot may be complex,and dead corners would occur when the vehicle density is high.These lead to pedestrians’sudden appearance in the vehicle’s path from an unexpected position,resulting in collision accidents in the parking lot.We advocate that besides vehicular sensing data,high-precision digital map of the parking lot,pedestrians’smart device’s sensing data,and attribute information of pedestrians can be used to detect the position of pedestrians in the parking lot.However,this subject has not been studied and explored in existing studies.Tofill this void,this paper proposes a pedestrian tracking framework integrating multiple information sources to provide pedestrian position and status information for vehicles and protect pedestrians in parking spaces.We also evaluate the proposed method through real-world experiments.The experimental results show that the proposed framework has its advantage in pedestrian attribute information extraction and positioning accuracy.It can also be used for pedestrian tracking in parking spaces.

Anti-occlusion pedestrian tracking algorithm based on location prediction and deep feature rematch

Aiming to the problem of pedestrian tracking with frequent or long-term occlusion in complex scenes,an anti-occlusion pedestrian tracking algorithm based on location prediction and deep feature rematch is proposed.Firstly,the occlusion judgment is realized by extracting and utilizing deep feature of pedestrian’s appearance,and then the scale adaptive kernelized correlation filter is introduced to implement pedestrian tracking without occlusion.Secondly,Karman filter is introduced to predict the location of occluded pedestrian position.Finally,the deep feature is used to the rematch of pedestrian in the reappearance process.Simulation experiment and analysis show that the proposed algorithm can effectively detect and rematch pedestrian under the condition of frequent or long-term occlusion.

Segmentation Based Real Time Anomaly Detection and Tracking Model for Pedestrian Walkways

Presently,video surveillance is commonly employed to ensure security in public places such as traffic signals,malls,railway stations,etc.A major chal-lenge in video surveillance is the identification of anomalies that exist in it such as crimes,thefts,and so on.Besides,the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian safety.The recent advances of Deep Learning(DL)models have received considerable attention in different processes such as object detec-tion,image classification,etc.In this aspect,this article designs a new Panoptic Feature Pyramid Network based Anomaly Detection and Tracking(PFPN-ADT)model for pedestrian walkways.The proposed model majorly aims to the recognition and classification of different anomalies present in the pedestrian walkway like vehicles,skaters,etc.The proposed model involves panoptic seg-mentation model,called Panoptic Feature Pyramid Network(PFPN)is employed for the object recognition process.For object classification,Compact Bat Algo-rithm(CBA)with Stacked Auto Encoder(SAE)is applied for the classification of recognized objects.For ensuring the enhanced results better anomaly detection performance of the PFPN-ADT technique,a comparison study is made using Uni-versity of California San Diego(UCSD)Anomaly data and other benchmark data-sets(such as Cityscapes,ADE20K,COCO),and the outcomes are compared with the Mask Recurrent Convolutional Neural Network(RCNN)and Faster Convolu-tional Neural Network(CNN)models.The simulation outcome demonstrated the enhanced performance of the PFPN-ADT technique over the other methods.

Bounding box based on probability density for the pedestrian tracking

This paper proposes a pedestrian tracking approach using bounding box based on probability densities.It is generally a difficult task to track features like corner points in outdoor images due to complex environment.To solve this problem,the feature points are projected along X and Y direction separately,and a histogram is constructed for each projection,with horizontal axis as positions and vertical axis as the number of feature points that lie on each position.Finally,the vertical axis is normalized for expression as probability.After histogram is constructed,the probability of each feature point is checked with a threshold.A feature point will be ignored if its probability is lower than a threshold,while the remaining feature points are grouped,based on which a bounding box is made.Kanade-Lucas Tomasi(KLT)algorithm is adopted as the tracking algorithm because it is able to track local features in images robustly.The efficiency of the tracking results using this method is verified in real environment test.

An Automated Player Detection and Tracking in Basketball Game

Vision-based player recognition is critical in sports applications.Accuracy,efficiency,and Low memory utilization is alluring for ongoing errands,for example,astute communicates and occasion classification.We developed an algorithm that tracks the movements of different players from a video of a basketball game.With their position tracked,we then proceed to map the position of these players onto an image of a basketball court.The purpose of tracking player is to provide the maximum amount of information to basketball coaches and organizations,so that they can better design mechanisms of defence and attack.Overall,our model has a high degree of identification and tracking of the players in the court.We directed investigations on soccer,basketball,ice hockey and pedestrian datasets.The trial comes about an exhibit that our technique can precisely recognize players under testing conditions.Contrasted and CNNs that are adjusted from general question identification systems,for example,Faster-RCNN,our approach accomplishes cutting edge exactness on three sorts of recreations(basketball,soccer and ice hockey)with 1000×fewer parameters.The all-inclusive statement of our technique is additionally shown on a standard passer-by recognition dataset in which our strategy accomplishes aggressive execution contrasted and cutting-edge methods.

Strong Tracking Particle Filter Based on the Chi-Square Test for Indoor Positioning

In recent years,a number of wireless indoor positioning(WIP),such as Bluetooth,Wi-Fi,and Ultra-Wideband(UWB)technologies,are emerging.However,the indoor environment is complex and changeable.Walls,pillars,and even pedestrians may block wireless signals and produce non-line-of-sight(NLOS)deviations,resulting in decreased positioning accuracy and the inability to provide people with real-time continuous indoor positioning.This work proposed a strong tracking particle filter based on the chi-square test(SPFC)for indoor positioning.SPFC can fuse indoor wireless signals and the information of the inertial sensing unit(IMU)in the smartphone and detect the NLOS deviation through the chi-square test to avoid the influence of the NLOS deviation on the final positioning result.Simulation experiment results show that the proposed SPFC can reduce the positioning error by 15.1%and 12.3% compared with existing fusion positioning systems in the LOS and NLOS environment.

基于车载视觉传感器的多目标检测跟踪技术研究

汽车在现代社会生活中扮演着举足轻重的角色,其极大地便利人们的出行,但与此同时,也引发诸如环境污染和交通拥堵等社会问题。为应对这些挑战,国内外学者正积极投身于无人驾驶技术的研究。其中,车辆与行人检测技术是无人驾驶技术的关键环节,但鉴于车辆与行人特征的多样性,使用单一的特征提取和分类方法进行检测变得异常困难。然而,深度学习的目标检测方案凭借其复杂的神经网络结构,成功攻克这一难题,因此备受国内外学者的关注。该文旨在通过深入剖析基于深度学习的目标检测以及基于SORT的多目标跟踪原理,为车载视觉传感器的研发创新提供有益的参考和启示。

Simulation of High Density Pedestrian Flow: A Microscopic Model

In recent years, modelling crowd and evacuation dynamics has become very important, with increasing huge numbers of people gathering around the world for many reasons and events. The fact that our global population grows dramatically every year and the current public transport systems are able to transport large amounts of people heightens the risk of crowd panic or crush. Pedestrian models are based on macroscopic or microscopic behaviour. In this paper, we are interested in developing models that can be used for evacuation control strategies. This model will be based on microscopic pedestrian simulation models, and its evolution and design requires a lot of information and data. The people stream will be simulated, based on mathematical models derived from empirical data about pedestrian flows. This model is developed from image data bases, so called empirical data, taken from a video camera or data obtained using human detectors. We consider the individuals as autonomous particles interacting through social and physical forces, which is an approach that has been used to simulate crowd behaviour. The target of this work is to describe a comprehensive approach to model a huge number of pedestrians and to simulate high density crowd behaviour in overcrowding places, e.g. sport, concert and pilgrimage places, and to assist engineering in the resolution of complicated problems through integrating a number of models from different research domains.

基于扩展卡尔曼滤波的交互式多模型跟踪算法研究

在辅助驾驶系统中,行人轨迹跟踪一直是一项有挑战性的任务,因为行人的回波信号中往往存在着许多干扰噪声。此外,行人在运动过程中可能会做出突然转身或其他改变方向的行为,这将直接导致行人运动轨迹呈现出非线性特征。针对上述问题,文中提出一种基于扩展卡尔曼滤波的交互式多模型跟踪(IMM-EKF)方法,适用于毫米波雷达对行人进行轨迹跟踪。首先,在扩展卡尔曼滤波算法(EKF)的基础上重构状态预测协方差矩阵,来补偿EKF非线性化过程中引入的误差;然后将改进的EKF作为交互式多模型算法(IMM)中的滤波器,根据行人运动特性选择匀速模型和协调转弯模型作为跟踪模型,利用所提出的IMM-EKF算法进行轨迹跟踪。实验结果表明,所提出的滤波算法较典型的EKF和改进的EKF算法,在跟踪滤波精度方面均有所提升,同时具备更优的跟踪鲁棒性。

基于域架构的纵横向结合行人紧急避障系统研究

针对行人横穿场景,提出了一种基于域架构的纵横向结合的紧急避障策略。首先,搭建基于域架构的行人紧急避障系统,通过最大减速度模型和多项式组,拟合纵横向轨迹决策规划方法,并基于采样离散数值计算法,综合考虑避障安全距离、动力学约束和平顺性的成本函数,选取最优轨迹;然后,分别使用双环比例-积分-微分(PID)、线性二次型调节器(LQR)控制方法,实现对纵横向的运动控制;最后,通过PreScan、CarSim和Simulink联合仿真及域架构系统延时分析,验证了该避障系统的适用性,有效提升了道路安全性。

抗遮挡的行人多目标跟踪算法

为了解决在复杂场景下行人相互遮挡导致跟踪系统精度降低的问题,提出了基于FairMOT的抗遮挡多目标跟踪算法(multiple obeject tracking algorithm with anti-occlusions,AOMOT)。首先通过轻量化平衡模块,解耦不同层次的语义信息,减少检测任务和重识别任务的语义冲突,降低重识别任务的性能提升对检测任务的影响。其次应用自注意力结构提取行人的外观特征,加强局部窗口下的类内特征的区分度,增强行人身份信息的匹配一致性并减少身份标识的频繁切换。最后优化身份关联算法,挖掘低置信度目标中的被遮挡对象,将其重新纳入目标身份关联并更新其重识别特征。实验结果表明,AOMOT相比原有FairMOT在MOT17数据集中高阶跟踪精度提升1.5百分点,身份F1分数提升3百分点,身份切换数量降低32%。

基于YOLOv8和DeepSort的多区域行人追踪算法研究

针对多摄像头重叠场景中行人追踪容易发生身份丢失、切换的问题,本文提出了一种基于YOLOv8和DeepSort的多摄像头跟踪算法.在检测阶段,利用无参注意力机制增强网络对行人特征的提取能力,提高了检测器的性能.在追踪阶段,通过提取两个摄像头的视角关键点,并计算出两个视角的单应性矩阵,实现了不同视角图像的拼接.通过利用目标间的单应性关系,在DeepSort算法中完成目标匹配.并在MOT15数据集中,对所改进的算法进行了测试.实验结果表明,本文提出的基于YOLOv8和DeepSort的改进算法的平均跟踪精确度为63.5%,比原始算法提升了3.4%.改进算法在行人身份切换次数方面减少了52次,比原始算法减少了6.5%.

基于检测和重识别的无人机行人跟踪算法

将智能检测跟踪算法与无人机(UAV)的灵活性相结合是UAV应用的研究热点。针对UAV的视角及运动导致目标滑移和遮挡的问题,提出一种基于检测和重识别的UAV行人跟踪算法。对训练好的YOLOv5进行TensorRT加速,解决UAV计算资源有限的问题;以量化加速的目标检测算法与重识别算法为基础,构建行人跟踪算法框架;设计判定行人匹配度,完成行人匹配系统设计。仿真试验表明:训练后的YOLOv5和OSNet具备一定的精度,采用TensorRT加速后的YOLOv5网络在保证精度的情况下,帧率有了近50%的提升。飞行试验表明:所提算法在行人穿插及障碍物遮挡的情况下,可以实现对目标的稳定跟踪,具备一定的实用性和有效性。

Indoor Pedestrian Tracking with Sparse RSS Fingerprints

Indoor pedestrian localization is of great importance for diverse mobile applications. Many indoor localization approaches have been proposed; among them, Radio Signal Strength （RSS）-based approaches have the advantage of existing infrastructures and avoid the cost of infrastructure deployment. However, the RSS-based localization approaches suffer from poor localization accuracy when the RSS fingerprints are sparse, as illustrated by actual experiments in this study. Here, we propose a novel indoor pedestrian tracking approach for smartphone users; this approach provides a high localization accuracy when the RSS fingerprints are sparse. Besides using the RSS fingerprints, this approach also utilizes the inertial sensor readings on smartphones. This approach has two components： （i） dead-reckoning subsystem that counts the number of walking steps with off-the-shelf inertial sensor readings on smartphones and （ii） particle filtering that computes the locations with only sparse RSS readings. The proposed approach is implemented on Android-based smartphones. Extensive experiments are carried out in both srnafl and large testbeds, The evaluation results show that the tracking approach can achieve a high accuracy of 5 m （up to 95%） in indoor environments with only sparse RSS fingerprints.

基于行为轨迹的中庭式酒店COVID-19感染概率研究

中庭式酒店建筑因中庭空间人员密集、气流波动大,属呼吸道传染疾病高感染概率区域,建筑高大公共空间的传染概率有待研究.以某中庭式酒店为研究对象,基于该酒店人流状况、行为轨迹的实测,通过回归分析,得到该酒店人流密度的分布规律;运用Anylogic软件,结合Wells-Riley感染概率计算模型,对不同类型人员感染后的酒店人群感染概率进行模拟.通过模拟可知,住宿人群、就餐人群、商务会议三类人群中单一人群感染时,感染概率分别为29.3%,17.9%,36.4%;当三类人员及后勤人员共同耦合感染时,感染概率增加至42.6%.通过对污染物颗粒的时空分布规律的分析发现三层、标准层污染物颗粒较多;人员交叉与感染概率呈正相关;流线交叉较多的就餐空间、滞留时间较长休闲空间、竖向交通空间等应重点考虑设计措施以降低其感染概率;该研究为人员行为轨迹下的酒店空间的布局优化提供研究思路,为疫情背景下寒冷地区中庭式酒店平疫结合酒店空间布局设计研究提供理论依据.

基于自监督部位感知的行人重识别模型及其在铁路客运站的应用

铁路客运站环境复杂,客流密集,一旦发生涉及旅客安全、影响站区运营等重要事件时,客运工作人员亟需快速掌握相关旅客的站内轨迹。为此,设计了一种基于自监督部位感知的行人重识别模型,基于该模型可实现对铁路客运站重点旅客的实时跟踪。从自监督部位感知预训练和行人重识别迁移学习两个方面详细阐述了模型的架构。试验表明,该模型在各类尤其是存在严重遮挡的行人重识别数据集上的性能均超越了通用的行人重识别模型。在中国铁路兰州局集团有限公司白银南站的现场试用表明,该模型可有效跟踪重点旅客在铁路客运站内的行进轨迹,为客运相关工作提供技术支持。