A genetic fuzzy system to model pedestrian walking path in a built environment

A study on the pedestrian’s steering behaviour through a built environment in normal circumstances is presented in this paper. The study focuses on the relationship between the environment and the pedestrian’s walking trajectory. Owing to the ambiguity and vagueness of the relationship between the pedestrians and the surrounding environment, a genetic fuzzy system is proposed for modelling and simulation of the pedestrian’s walking trajectory confronting the environmental stimuli. We apply the genetic algorithm to search for the optimum membership function parameters of the fuzzy model. The proposed system receives the pedestrian’s perceived stimuli from the environment as the inputs, and provides the angular change of direction in each step as the output. The environmental stimuli are quantified using the Helbing social force model. Attractive and repulsive forces within the environment represent various environmental stimuli that influence the pedestrian’s walking trajectory at each point of the space. To evaluate the effectiveness of the proposed model, three experiments are conducted. The first experimental results are validated against real walking trajectories of participants within a corridor. The second and third experimental results are validated against simulated walking trajectories collected from the AnyLogic® software. Analysis and statistical measurement of the results indicate that the genetic fuzzy system with optimised membership functions produces more accurate and stable prediction of heterogeneous pedestrians’ walking trajectories than those from the original fuzzy model.     

面向行人导航的RMLT DR算法研究与实现

DR算法是行人导航中最常用的一种推算算法。分析常规DR算法,针对行人导航中DR算法是固定阈值,不能根据行人环境不同而自动调整阂值,导致行人定位精确度不高的缺点,提出了基于雷达的多级阈值DR算法,即RMLT DR算法。通过仿真模拟实验,对比分析了RMLT DR算法和常规DR算法的定位结果。验证了行人行走过程中,RMLT DR算法可以根据周围的环境自动选择阈值大小,具有更高的精确度。     

Modeling pedestrians’ interest in locations: A concept to improve simulations of pedestrian destination choice

Large environments that are designed for travel, leisure, and for everyday life – such as transport hubs, amusement parks, and shopping centers – feature different locations that are frequently visited by pedestrians. Each visit is evoked by one’s motivation to engage in some kind of activity at a certain location. By means of modeling the pedestrians’ interests in locations with the aid of computer simulations, it is possible to forecast the occupancy at locations by utilizing sophisticated pedestrian destination choice models. In the field of pedestrian dynamics research, location preference modeling is not common, but it is all the more rare to include a psychological grounding into such choice models. Here we show that our psychologically inspired and mathematically defined model to describe pedestrians’ interests in locations is able to improve the exactness of pedestrian destination choice models. The interest function model is based on the psychological concept of goal-related memory accessibility and on fundamental coherences found in pedestrian-related data that is measurable at locations. We validated the interest function model and our results provide evidence that our approach improves the simulation fidelity regarding occupancy forecasting. Because the interest concept is designed as a framework that can be coupled to existing microscopic pedestrian simulators, it can be used in most pedestrian destination choice models to describe pedestrian visiting preferences. Consequently, the reliability of the occupancy predictions of pedestrian simulations can be enhanced by integrating the interest function model into choices models.     

车辆辅助驾驶系统中基于计算机视觉的行人检测研究综述

基于计算机视觉的行人检测由于其在车辆辅助驾驶系统中的重要应用价值成为当前计算机视觉和智能车辆领域最为活跃的研究课题之一. 其核心是利用安装在运动车辆上的摄像机检测行人，从而估计出潜在的危险以便采取策略保护行人.本文在对这一问题存在的困难进行分析的基础上，对相关文献进行综述. 基于视觉的行人检测系统一般包括两个模块:感兴趣区分割和目标识别，本文介绍了这两个模块所采用的一些典型方法，分析了每种方法的原理和优缺点. 最后对性能评估和未来的研究方向等一系列关键问题给予了介绍.     

Associated evolution of a support vector machine-based classifier for pedestrian detection

Support vector machine (SVM) has become a dominant classification technique used in pedestrian detection systems. In such systems, classifiers are used to detect pedestrians in some input frames. The performance of a SVM classifier is mainly influenced by two factors: the selected features and the parameters of the kernel function. These two factors are highly related and therefore, it is desirable that the two factors can be analyzed simultaneously, which are usually not the case in the previous work.In this paper, we propose an evolutionary method to simultaneously optimize the feature set and the parameters for the SVM classifier. Specifically, adaptive genetic operators were designed to be suitable for the feature selection and parameter tuning. The proposed method is used to train a SVM classifier for pedestrian detection. Experiments in real city traffic scenes show that the proposed approach leads to higher detection accuracy and shorter detection time.     

Recognition of pedestrian trajectories and attributes with computer vision and deep learning techniques

Analyzing the walking behavior of the public is vital for revealing the need for infrastructure design in a local neighborhood, supporting human-centric urban area development. Traditional walking behavior analysis practices relying on manual on-street surveys to collect pedestrian flow data are labor-intensive and tedious. On the contrary, automated video analytics using surveillance cameras based on computer vision and deep learning techniques appears more effective in generating pedestrian flow statistics. Nevertheless, most existing methods of pedestrian tracking and attribute recognition suffer from several challenging conditions, such as inter-person occlusion and appearance variations, which leads to ambiguous identities and hence inaccurate pedestrian flow statistics.Therefore, this paper proposes a more robust methodology of pedestrian tracking and attribute recognition, facilitating the analysis of pedestrian walking behavior. Specific limitations of a current state-of-the-art method are inferred, based on which several improvement strategies are proposed: 1) incorporating high-level pedestrian attributes to enhance pedestrian tracking, 2) a similarity measure integrating multiple cues for identity matching, and 3) a probation mechanism for more robust identity matching. From our evaluation using two public benchmark datasets, the developed strategies notably enhance the robustness of pedestrian tracking against the challenging conditions mentioned above. Subsequently, the outputs of trajectories and attributes are aggregated into fine-grained pedestrian flow statistics among different pedestrian groups. Overall, our developed framework can support a more comprehensive and reliable decision-making for human-centric planning and design in different urban areas. The framework is also applicable to exploiting pedestrian movement patterns in different scenes for analyses such as urban walkability evaluation. Moreover, the developed mechanisms are generalizable to future researches as a baseline, which provides generic insights of how to fundamentally enhance pedestrian tracking.     

Locality guided cross-modal feature aggregation and pixel-level fusion for multispectral pedestrian detection

Multispectral pedestrian detection has received much attention in recent years due to its superiority in detecting targets under adverse lighting/weather conditions. In this paper, we aim to generate highly discriminative multi-modal features by aggregating the human-related clues based on all available samples presented in multispectral images. To this end, we present a novel multispectral pedestrian detector performing locality guided cross-modal feature aggregation and pixel-level detection fusion. Given a number of single bounding boxes covering pedestrians in both modalities, we deploy two segmentation sub-branches to predict the existence of pedestrians on visible and thermal channels. By referring to the important locality information in the reference modality, we perform locality guided cross-modal feature aggregation to learn highly discriminative human-related features in the complementary modality by exploring the clues of all available pedestrians. Moreover, we utilize the obtained spatial locality maps to provide prediction confidence scores in visible and thermal channels and conduct pixel-wise adaptive fusion of detection results in complementary modalities. Extensive experiments demonstrate the effectiveness of our proposed method, outperforming the current state-of-the-art detectors on both KAIST and CVC-14 multispectral pedestrian detection datasets.     

Simulating operational behaviors of pedestrian navigation

Navigation is an innate ability for humans, but simulating this capability in a virtual environment is no easy task and has been of interest to researchers for over a decade. This paper describes the development of ISAPT, an individual-based Intermodal Simulator for the Analysis of Pedestrian Traffic. ISAPT’s development is based on the observed behaviors of pedestrians reported from the literature and simulates the strategies employed by pedestrians for collision avoidance, including changes in speed and trajectory, passing strategies, and distance between objects. The implementation of these behaviors and strategies is described in the paper along with the results from a validation study. These results illustrate that the micro-level simulation of individual pedestrians gives ISAPT the ability to reproduce identified macro-level pedestrian behavior, as well as the capability to reproduce the operational statistics of an observed pedestrian corridor. Such functionality is necessary to support the use of simulation as a tool for designers and planners in the design and evaluation of intermodal facilities.     

Analysis of traffic hazard intensity: A spatial epidemiology case study of urban pedestrians

Traffic safety studies have underscored the hazardous conditions of pedestrians in the United States. This situation calls for increased public awareness of the pedestrian safety issue and better knowledge of the main factors contributing to traffic hazard for urban pedestrians. The purpose of this spatial epidemiology research is to gain greater insights into the geographic dimension exhibited by the intensity of traffic collisions involving urban pedestrians. Pedestrian crashes are studied in Buffalo, NY for years 2003 and 2004. Factors of hazard intensity are determined and compared for three age cohorts as well as for collisions occurring at intersections versus mid-block locations. Physical road characteristics and density of development, as well as socio-economic and demographic variables and potential trip attractors are examined. Spatial regression models are used to account for spatial dependencies. Econometric analysis underscores that all classes of environmental factors tested are significant drivers of pedestrian traffic hazard intensity. Results of the geographic analysis indicate that young and adult pedestrian traffic hazard intensities follow rather distinct logics. In addition, intersection and mid-block crashes differ by their socio-economic correlates, as well as their spatial distribution in the urban fabric.     

A framework for pedestrian comfort navigation using multi-modal environmental sensors

Environments significantly influence the sensation of pedestrians, while sensing and navigation technologies can help people improve their trip comfort. In this paper, we present an integrated framework, named NaviComf, which constructs pedestrian navigation systems to improve comfort in time varying environments taking into account the heterogeneous environmental factors. With NaviComf we aim to systematically provide solutions to the four key issues: (1) how to organize the huge amount of sensor data, (2) how to forecast future environmental information, (3) how to incorporate the heterogeneous environmental factors, and (4) how to select optimal paths in time varying environments. We have gathered sensor data of air temperature, relative humidity, and pedestrian congestion in real environments. We have also implemented a prototype system on the basis of the framework using the sensor data. Results of simulations and evaluations show that NaviComf can efficiently provide more comfortable paths as compared with the traditional navigation method.     

Prediction of pedestrians routes within a built environment in normal conditions

Modelling and prediction of pedestrian routing behaviours within known built environments has recently attracted the attention of researchers across multiple disciplines, owing to the growing demand on urban resources and requirements for efficient use of public facilities. This study presents an investigation into pedestrians’ routing behaviours within an indoor environment under normal, non-panic situations. A network-based method using constrained Delaunay triangulation is adopted, and a utility-based model employing dynamic programming is developed. The main contribution of this study is the formulation of an appropriate utility function that allows an effective application of dynamic programming to predict a series of consecutive waypoints within a built environment. The aim is to generate accurate sequence waypoints for the pedestrian walking path using only structural definitions of the environment as defined in a standard CAD format. The simulation results are benchmarked against those from the A¹ algorithm, and the outcome positively indicates the usefulness of the proposed method in predicting pedestrians’ route selection activities.     

Rapid pedestrian detection in unseen scenes

In this paper, a rapid adaptive pedestrian detection method based on cascade classifier with ternary pattern is proposed. The proposed method achieves its goal by employing the following three new strategies: (1) A method for adjusting the key parameters of the trained cascade classifier dynamically for detecting pedestrians in unseen scenes using only a small amount of labeled data from the new scenes. (2) An efficient optimization method is proposed, based on the cross entropy method and a priori knowledge of the scenes, to solve the classifier parameter optimization problem. (3) In order to further speed up pedestrian detection in unseen scenes, each strong classifier in the cascade employs a ternary detection pattern. In our experiments, two significantly different datasets, AHHF and NICTA, were used as the training set and testing set, respectively. The experimental results showed that the proposed method can quickly adapt a previously trained detector for pedestrian detection in various scenes compared with other existing methods.     

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. To fill 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.     

Modeling vehicle–pedestrian interactions outside of crosswalks

Pedestrian and driver behaviors as well as their interactions, are essential in planning, designing and operating highway facilities. Pedestrian crossing outside of a marked or unmarked crosswalk (i.e. jaywalking), is one of those pedestrian behaviors that may highly affect safety and operations. Unlike permissible crossings at crosswalks, jaywalking events are not often anticipated by drivers, which may result in less driver reaction time and different vehicle operation dynamics. It is important to understand pedestrian crossing behavior outside of crosswalks, as well as driver yielding behavior towards them. To date, limited quantitative and behavioral research has been conducted to investigate this interaction or simulate it microscopically. This paper aims to explore both pedestrian jaywalking behavior (gap acceptance and speeds) and the corresponding driver reactions (yielding behavior) for modeling the vehicle–pedestrian interactions (VPI) outside the crosswalks in a micro-simulation environment. The study also quantifies the differences between vehicle–jaywalker and vehicle-permissible crossing. An observational study and an instrumented vehicle study were conducted on the campus of the University of Florida to collect data from pedestrian and driver perspectives, respectively.Crossing speed, yield acceptance and delay of jaywalking crossings and permissible crossings were observed in the study and these attributed can be used for replicating pedestrian operations in simulators. Moreover, behaviors of driver approaching jaywalkers versus pedestrians crossing at designated crosswalks were compared on the basis of yield rates, and vehicle speed profiles. Vehicle yield dynamics were analyzed to model the driver reactions towards jaywalkers. Lastly, it was found that the locations of jaywalking events are highly concentrated and influenced by the crossing environment, such as pedestrian and vehicular volume, bus stops presence and crossing distance.This paper establishes several quantitative relationships describing interactions between pedestrians crossing outside of crosswalks and approaching drivers, which provide the basis and assumptions for modeling such interactions in a micro-simulation environment for traffic operational analyses.     

结合语义和多层特征融合的行人检测

遮挡及背景中相似物干扰是行人检测准确率较低的主要原因.针对该问题,提出一种结合语义和多层特征融合(Combining semantics with multi-level feature fusion,CSMFF)的行人检测算法.首先,融合多个卷积层特征,并在融合层上添加语义分割,得到的语义特征与相应的卷积层连接作为行...     

融合先验知识的自适应行人跟踪算法

在实际监控场合中,行人的运动有着诸多不确定性,这些会对现有的跟踪算法产生干扰,从而造成跟踪丢失.基于此,文中提出一种将行人检测的先验知识融入到跟踪模型自学习过程的行人跟踪算法.首先通过离线训练,得到具有较强区分能力的子分类器集,这些子分类器蕴含了对于行人的先验知识.在跟踪过程中,使用online boosting算法从离线训练的子分类器集中学习并更新强分类器,对被跟踪行人进行动态建模.实验结果表明,该算法有效缓解算法自适应性与"漂移"之间的矛盾,能够在真实监控场合下跟踪具有复杂运动的行人.     

Pedestrian trajectory prediction using BiRNN encoder–decoder framework*

Autonomous mobile robots navigating through human crowds are required to foresee the future trajectories of surrounding pedestrians and accordingly plan safe paths to avoid any possible collision. This paper presents a novel approach for pedestrian trajectory prediction. In particular, we developed a new method based on an encoder–decoder framework using bidirectional recurrent neural networks (BiRNN). The difficulty of incorporating social interactions into the model has been addressed thanks to the special structure of BiRNN enhanced by the attention mechanism, a proximity-independent model of the relative importance of each pedestrian. The main difference between our and the previous approaches is that BiRNN allows us to employs information on the future state of the pedestrians. We tested the performance of our method on several public datasets. The proposed model outperforms the current state-of-the-art approaches on most of these datasets. Furthermore, we analyze the resulting predicted trajectories and the learned attention scores to prove the advantages of BiRRNs on recognizing social interactions.     

Protecting the privacy of humans in video sequences using a computer vision-based de-identification pipeline

We propose a computer vision-based de-identification pipeline that enables automated protection of privacy of humans in video sequences through obfuscating their appearance, while preserving the naturalness and utility of the de-identified data. Our pipeline specifically addresses de-identifying soft and non-biometric features, such as clothing, hair, skin color etc., which often remain recognizable when simpler techniques such as blurring are applied. Assuming a surveillance scenario, we combine background subtraction based on Gaussian mixtures with an improved version of the GrabCut algorithm to find and segment pedestrians. De-identification is performed by altering the appearance of the segmented pedestrians through the neural art algorithm that uses the responses of a deep neural network to render the pedestrian images in a different style. Experimental evaluation is performed both by automated classification and through a user study. Results suggest that the proposed pipeline successfully de-identifies a range of hard and soft biometric and non-biometric identifiers, including face, clothing and hair.