After establishing a CSI acquisition system , we process CSI data guided by theoretical derivation under different corridor scenarios and finally obtain the Doppler shift of different relative motions . Our results demonstrate that this method of measuring Doppler shift could be applied to further research on crowd counting or human activity recognition based on WiFi . Counting people and estimating their number is a fundamental task for many intelligent security systems , including CCTV systems and other visual surveillance research areas . This paper presents a new approach for crowd counting . The proposed method is independent of any background modelling or background subtraction techniques . Crowd counting problem aims to count the number of objects within an image or a frame in the videos and is usually solved by estimating the density map generated from the object location annotations . The values in the density map , by nature , take two possible states : zero indicating no object around , a non-zero value indicating the existence of objects and the value denoting the local object density . CNN is introduced for the task of feature learning and deep decision forest is extended to address label distribution learning problem in crowd counting . Thereinto , the proposed network replaces its softmax layer with the aforementioned probabilistic decision forest in order to better establish a mapping relationship between image features and crowds ’ number so as to implement an end-to-end hybrid model for crowd counting problem . Our method demonstrated in the final experiments not only attains the high accuracy in crowd counting but has comparable robustness and instantaneity in selected public datasets as well . Crowd counting is the task of estimating people numbers in crowd images . Modern crowd counting methods employ deep neural networks to estimate crowd counts via crowd density regressions . A major challenge of this task lies in the perspective distortion , which results in drastic person scale change in an image . Crowd counting is the task of estimating pedestrian numbers in crowd images . Modern crowd counting methods employ deep neural networks to estimate crowd counts via crowd density regressions . A major challenge of this task lies in the drastic changes of scales and perspectives in images . However , once general deep methods are investigated from large datasets , they can be consider to investigate different crowd videos and images . Therefore , it would be able to cope with the problem including to not limited to crowd density estimation , crowd people counting , and crowd event recognition . Deep learning models and approaches are required to have large datasets for training and testing . The task of crowd counting and density map estimation is riddled with many challenges such as occlusions , non-uniform density , intra-scene and inter-scene variations in scale and perspective . Nevertheless , over the last few years , crowd count analysis has evolved from earlier methods that are often limited to small variations in crowd density and scales to the current state-of-the-art methods that have developed the ability to perform successfully on a wide range of scenarios . The success of crowd counting methods in the recent years can be largely attributed to deep learning and publications of challenging datasets . Wi-Fi in smartphones are designed to periodically transmit probe-request-frame to determine when a known access point is within range and by capitalizing this Wi-Fi behavior , crowd counting and analysis have been done by continuous monitoring and counting these Wi-Fi frames . The proliferation of Wi-Fi enabled mobile devices and the ever-increasing number of mobile devices in use , suggests opportunities for developing lowcost crowd counting and analysis solution . A Multi-Stream Convolutional Neural Network is developed and evaluated in this work , which receives an image as input and produces a density map that represents the spatial distribution of people in an end-to-end fashion . In order to address complex crowd counting issues , such as extremely unconstrained scale and perspective changes , the network architecture utilizes receptive fields with different size filters for each stream . In addition , we investigate the influence of the two most common fashions on the generation of ground truths and propose a hybrid method based on tiny face detection and scale interpolation . It does not need any human intervention so it can be used successfully in a fully automated crowd control systems . Two new features are introduced for crowd counting purpose : the first represents thermal characteristics of humans and is expressed by the ratio between their temperature and their ambient environment temperature . The second describes humans motion characteristics and is measured by the ratio between humans motion velocity and the ambient environment rigidity . In this work , we explore the cross-scale similarity in crowd counting scenario , in which the regions of different scales often exhibit high visual similarity . This feature is universal both within an image and across different images , indicating the importance of scale invariance of a crowd counting model . Visual crowding is a breakdown in object identification that occurs in cluttered scenes , a process that represents the principle restriction on visual performance in the periphery . When crowded objects are presented experimentally , a key finding is that observers frequently report nearby flanking items instead of the target . The goal of this book isto provide the readers a comprehensive map towards the common goal of better analyzing and synthesizing the pedestrian movement in dense , heterogeneous crowds . Thebook is organized into different parts that consolidate various aspects of research towards this common goal , namely the modeling , simulation , and visual analysis of crowds . Through this book , readers will see the common ideas and vision as well as the different challenges and techniques , that will stimulate novel approaches to fully grasping crowds . " In addition we introduce various pieces of work that appear in this monograph as separate chapters . S. Ali ( ) Center for Vision Technologies , SRI International , 201 Washington Road , Princeton , NJ , USA e-mail : saad.ali@sri.com K. Nishino Department of Computer Science , Drexel University , 3141 Chestnut Street , Philadelphia , PA 19104 , USA e-mail : kon@drexel.edu D. Manocha University of North Carolina , Chapel Hill , NC , USA e-mail : dm@cs.unc.edu M. Shah University of Central Florida , Orlando , FL , USA e-mail : shah@crcv.ucf.edu S. Ali et al. ( eds . ) , Modeling , Simulation and Visual Analysis of Crowds , The International Series in Video Computing 11 , DOI 10.1007/978 - 1 - 4614 - 8483 - 7__1 , © Springer Science+Business Media New York 2013 1 Video imagery based crowd analysis for population profiling and density estimation in public spaces can be a highly effective tool for establishing global situational awareness . Different strategies such as counting by detection and counting by clustering have been proposed , and more recently counting by regression has also gained considerable interest due to its feasibility in handling relatively more crowded environments . This thesis looks to advance understanding in the field of computer vision based crowd analysis through a combination of deep learning techniques , multi-task learning , and domain adaptation . Issues that have limited progress in this field to date include visual occlusion , scale and perspective issues , variation in scene content as well as a lack of labelled training data . Given a simulation , we formulate the crowd analysis problem as an anomaly detection problem and exploit state‐of‐the‐art outlier detection algorithms to address it . To that end , we introduce a new framework for the visual analysis of crowd simulations . Our framework allows us to capture potentially erroneous behaviors on a per‐agent basis either by automatically detecting outliers based on individual evaluation metrics or by accounting for multiple evaluation criteria in a principled fashion using Principle Component Analysis and the notion of Pareto Optimality . Crowd counting and density estimation is an important and challenging problem in the visual analysis of the crowd . Most of the existing approaches use regression on density maps for the crowd count from a single image . With multiple crowd gatherings of millions of people every year in events ranging from pilgrimages to protests , concerts to marathons , and festivals to funerals ; visual crowd analysis is emerging as a new frontier in computer vision . In particular , counting in highly dense crowds is a challenging problem with far-reaching applicability in crowd safety and management , as well as gauging political significance of protests and demonstrations . The Processing language is widely used by artist and designers of varying levels of expertise and thus fulfils these conditions . Here , we investigated whether the Processing inspired JavaScript library p5.js can be used for visual crowd research . We specifically explored how to use p5.js in combination with MTurk and report a simple way of using p5.js ' sketches ' to perform online tests . A novel hierarchical model is introduced to solve a general problem of detecting groups of similar objects . Under this model , detection of groups is performed in hierarchically organized layers while each layer represents a scope for target objects . The processing of these layers involves sequential extraction of appearance features for an individual object , consistency measurement features for nearby objects , and finally the distribution features for all objects within the group . Most of the behaviors people exhibit while being part of a crowd are social processes that tend to emerge among groups and as a consequence , detecting groups in crowds is becoming an important issue in modern behavior analysis . We propose a supervised correlation clustering technique that employs Structural SVM and a proxemic based feature to learn how to partition people trajectories in groups , by injecting in the model socially plausible shape configurations . Using previous works on emotion modelling and virtual crowds we define a game world in an urban context . To achieve that , we explore a biologically inspired human emotion model , investigate the formation of groups in crowds , and examine the use of physics middleware for crowds . Furthermore , we assess the realism and computational performance of the proposed approach . Here , we focus on pedestrians whose limbs and lower body parts often get occluded in crowded scene . We first investigate the unmet challenges of pedestrian tracking in crowds and propose a challenging video to evaluate segmentation-tracking robustness to inter-pedestrian occlusions . We then detail a fast segmentation-based method to overcome some aspects of the tracking-under-occlusion problem . Whereas crowdsourcing as a topic has often been addressed in recent literature , web-based crowdworking platforms that manage the interface between crowdsourcers and crowdworkers have not received much attention so far . Furthermore , most of these platforms focus on either the management of external or internal crowds ; platforms that handle both groups are rare . This paper investigates such a provider : the German company Across Systems . Finally our framework can be used to produce real-life collective social behaviors over crowds , including community-guided flocking , leader following , and spatio-social information propagation . Social Flocks can serve as visualization of simulated crowds for domain experts to explore the dynamic effects of the spatial , temporal , and social factors on social networks . In addition , it provides an experimental platform of collective social behaviors for social gaming and movie animations . We present an approach for the solution of this problem that is based on Contraction Theory , a novel framework for the analysis of the stability complex nonlinear dynamical systems . Using a learning-based realtime-capable architecture for the animation of crowds , we demonstrate the application of this novel approach for the stability design for the groups of characters that interact in various ways . The underlying dynamics specifies control rules for propagation speed and direction , and for the synchronization of the gait phases . This may be particularly advantageous whenever the computational costs of stochastic simulation may only be justified when a final system design is considered , but would be prohibitive when exploring many design options and conditions during early phases of design . The analysis of the threshold for the self-organisation of crowds in a city is an example of such explorative analysis that would require many simulations to obtain an accurate view of the phase-transition . This is even more so when more complex city-topologies and a large number of agents are considered . Pedestrian detection in crowded scenes is an intractable problem in computer vision , in which occlusion often presents a great challenge . In this paper , we propose a novel context-aware feature learning method for detecting pedestrians in crowds , with the purpose of making better use of context information for dealing with occlusion . Unlike most current pedestrian detectors that only extract context information from a single and fixed region , a new pixel-level context embedding module is developed to integrate multi-cue context into a deep CNN feature hierarchy , which enables access to the context of various regions by multi-branch convolution layers with different receptive fields . This paper focus on the research on visual technology of the 3D crowds and emphasizes how to decrease the feature loss of model , then presents a model simplification method to realize crowds rendering based on LOD . We evaluate our approach over millions of queries from a commercial search engine ( i.e. , Baidu Map ) . Experimental results show that the proposed approach provides significant performance improvements over the competitive existing methods in terms of classical metrics ( i.e. , Recall@K and MRR ) as well as the prediction of crowds ’ search missions . With agent-based method then each virtual agents can have different behavior and abilities . Optimal reciprocal collision avoidance(ORCA ) which is the best version of Reciprocal velocity obstacle(RVO ) used as virtual agents collision avoidance in crowds simulation . Experiment result demonstrates the simulation of hundreds of agents in a three evacuation scenario . This overlay network will provide scalable and balanced access to the students and teachers to e-learning facilities . The CCDN will be composed by caches used as edge delivery nodes , content routing capabilities to route the client to the closest copy of the content , distributed content distribution and management and improving the access of users in terms of speed , reliability , scalability and prevention of flash-crowds . We present as a case study the work in progress of the deployment of a CCDN in the technical university of Valencia together with an e-learning streaming application over the CCDN .
After establishing a CSI acquisition system , we process CSI data guided by theoretical derivation under different corridor scenarios and finally obtain the Doppler shift of different relative motions . Our results demonstrate that this method of measuring Doppler shift could be applied to further research on crowd counting or human activity recognition based on WiFi .
Counting people and estimating their number is a fundamental task for many intelligent security systems , including CCTV systems and other visual surveillance research areas . This paper presents a new approach for crowd counting . The proposed method is independent of any background modelling or background subtraction techniques .
Crowd counting problem aims to count the number of objects within an image or a frame in the videos and is usually solved by estimating the density map generated from the object location annotations . The values in the density map , by nature , take two possible states : zero indicating no object around , a non-zero value indicating the existence of objects and the value denoting the local object density .
CNN is introduced for the task of feature learning and deep decision forest is extended to address label distribution learning problem in crowd counting . Thereinto , the proposed network replaces its softmax layer with the aforementioned probabilistic decision forest in order to better establish a mapping relationship between image features and crowds ’ number so as to implement an end-to-end hybrid model for crowd counting problem . Our method demonstrated in the final experiments not only attains the high accuracy in crowd counting but has comparable robustness and instantaneity in selected public datasets as well .
Crowd counting is the task of estimating people numbers in crowd images . Modern crowd counting methods employ deep neural networks to estimate crowd counts via crowd density regressions . A major challenge of this task lies in the perspective distortion , which results in drastic person scale change in an image .
Crowd counting is the task of estimating pedestrian numbers in crowd images . Modern crowd counting methods employ deep neural networks to estimate crowd counts via crowd density regressions . A major challenge of this task lies in the drastic changes of scales and perspectives in images .
However , once general deep methods are investigated from large datasets , they can be consider to investigate different crowd videos and images . Therefore , it would be able to cope with the problem including to not limited to crowd density estimation , crowd people counting , and crowd event recognition . Deep learning models and approaches are required to have large datasets for training and testing .
The task of crowd counting and density map estimation is riddled with many challenges such as occlusions , non-uniform density , intra-scene and inter-scene variations in scale and perspective . Nevertheless , over the last few years , crowd count analysis has evolved from earlier methods that are often limited to small variations in crowd density and scales to the current state-of-the-art methods that have developed the ability to perform successfully on a wide range of scenarios . The success of crowd counting methods in the recent years can be largely attributed to deep learning and publications of challenging datasets .
Wi-Fi in smartphones are designed to periodically transmit probe-request-frame to determine when a known access point is within range and by capitalizing this Wi-Fi behavior , crowd counting and analysis have been done by continuous monitoring and counting these Wi-Fi frames . The proliferation of Wi-Fi enabled mobile devices and the ever-increasing number of mobile devices in use , suggests opportunities for developing lowcost crowd counting and analysis solution .
A Multi-Stream Convolutional Neural Network is developed and evaluated in this work , which receives an image as input and produces a density map that represents the spatial distribution of people in an end-to-end fashion . In order to address complex crowd counting issues , such as extremely unconstrained scale and perspective changes , the network architecture utilizes receptive fields with different size filters for each stream . In addition , we investigate the influence of the two most common fashions on the generation of ground truths and propose a hybrid method based on tiny face detection and scale interpolation .
It does not need any human intervention so it can be used successfully in a fully automated crowd control systems . Two new features are introduced for crowd counting purpose : the first represents thermal characteristics of humans and is expressed by the ratio between their temperature and their ambient environment temperature . The second describes humans motion characteristics and is measured by the ratio between humans motion velocity and the ambient environment rigidity .
In this work , we explore the cross-scale similarity in crowd counting scenario , in which the regions of different scales often exhibit high visual similarity . This feature is universal both within an image and across different images , indicating the importance of scale invariance of a crowd counting model .
Visual crowding is a breakdown in object identification that occurs in cluttered scenes , a process that represents the principle restriction on visual performance in the periphery . When crowded objects are presented experimentally , a key finding is that observers frequently report nearby flanking items instead of the target .
The goal of this book isto provide the readers a comprehensive map towards the common goal of better analyzing and synthesizing the pedestrian movement in dense , heterogeneous crowds . Thebook is organized into different parts that consolidate various aspects of research towards this common goal , namely the modeling , simulation , and visual analysis of crowds . Through this book , readers will see the common ideas and vision as well as the different challenges and techniques , that will stimulate novel approaches to fully grasping crowds . "
In addition we introduce various pieces of work that appear in this monograph as separate chapters . S. Ali ( ) Center for Vision Technologies , SRI International , 201 Washington Road , Princeton , NJ , USA e-mail : saad.ali@sri.com K. Nishino Department of Computer Science , Drexel University , 3141 Chestnut Street , Philadelphia , PA 19104 , USA e-mail : kon@drexel.edu D. Manocha University of North Carolina , Chapel Hill , NC , USA e-mail : dm@cs.unc.edu M. Shah University of Central Florida , Orlando , FL , USA e-mail : shah@crcv.ucf.edu S. Ali et al. ( eds . ) , Modeling , Simulation and Visual Analysis of Crowds , The International Series in Video Computing 11 , DOI 10.1007/978 - 1 - 4614 - 8483 - 7__1 , © Springer Science+Business Media New York 2013 1
Video imagery based crowd analysis for population profiling and density estimation in public spaces can be a highly effective tool for establishing global situational awareness . Different strategies such as counting by detection and counting by clustering have been proposed , and more recently counting by regression has also gained considerable interest due to its feasibility in handling relatively more crowded environments .
This thesis looks to advance understanding in the field of computer vision based crowd analysis through a combination of deep learning techniques , multi-task learning , and domain adaptation . Issues that have limited progress in this field to date include visual occlusion , scale and perspective issues , variation in scene content as well as a lack of labelled training data .
Given a simulation , we formulate the crowd analysis problem as an anomaly detection problem and exploit state‐of‐the‐art outlier detection algorithms to address it . To that end , we introduce a new framework for the visual analysis of crowd simulations . Our framework allows us to capture potentially erroneous behaviors on a per‐agent basis either by automatically detecting outliers based on individual evaluation metrics or by accounting for multiple evaluation criteria in a principled fashion using Principle Component Analysis and the notion of Pareto Optimality .
Crowd counting and density estimation is an important and challenging problem in the visual analysis of the crowd . Most of the existing approaches use regression on density maps for the crowd count from a single image .
With multiple crowd gatherings of millions of people every year in events ranging from pilgrimages to protests , concerts to marathons , and festivals to funerals ; visual crowd analysis is emerging as a new frontier in computer vision . In particular , counting in highly dense crowds is a challenging problem with far-reaching applicability in crowd safety and management , as well as gauging political significance of protests and demonstrations .
The Processing language is widely used by artist and designers of varying levels of expertise and thus fulfils these conditions . Here , we investigated whether the Processing inspired JavaScript library p5.js can be used for visual crowd research . We specifically explored how to use p5.js in combination with MTurk and report a simple way of using p5.js ' sketches ' to perform online tests .
A novel hierarchical model is introduced to solve a general problem of detecting groups of similar objects . Under this model , detection of groups is performed in hierarchically organized layers while each layer represents a scope for target objects . The processing of these layers involves sequential extraction of appearance features for an individual object , consistency measurement features for nearby objects , and finally the distribution features for all objects within the group .
Most of the behaviors people exhibit while being part of a crowd are social processes that tend to emerge among groups and as a consequence , detecting groups in crowds is becoming an important issue in modern behavior analysis . We propose a supervised correlation clustering technique that employs Structural SVM and a proxemic based feature to learn how to partition people trajectories in groups , by injecting in the model socially plausible shape configurations .
Using previous works on emotion modelling and virtual crowds we define a game world in an urban context . To achieve that , we explore a biologically inspired human emotion model , investigate the formation of groups in crowds , and examine the use of physics middleware for crowds . Furthermore , we assess the realism and computational performance of the proposed approach .
Here , we focus on pedestrians whose limbs and lower body parts often get occluded in crowded scene . We first investigate the unmet challenges of pedestrian tracking in crowds and propose a challenging video to evaluate segmentation-tracking robustness to inter-pedestrian occlusions . We then detail a fast segmentation-based method to overcome some aspects of the tracking-under-occlusion problem .
Whereas crowdsourcing as a topic has often been addressed in recent literature , web-based crowdworking platforms that manage the interface between crowdsourcers and crowdworkers have not received much attention so far . Furthermore , most of these platforms focus on either the management of external or internal crowds ; platforms that handle both groups are rare . This paper investigates such a provider : the German company Across Systems .
Finally our framework can be used to produce real-life collective social behaviors over crowds , including community-guided flocking , leader following , and spatio-social information propagation . Social Flocks can serve as visualization of simulated crowds for domain experts to explore the dynamic effects of the spatial , temporal , and social factors on social networks . In addition , it provides an experimental platform of collective social behaviors for social gaming and movie animations .
We present an approach for the solution of this problem that is based on Contraction Theory , a novel framework for the analysis of the stability complex nonlinear dynamical systems . Using a learning-based realtime-capable architecture for the animation of crowds , we demonstrate the application of this novel approach for the stability design for the groups of characters that interact in various ways . The underlying dynamics specifies control rules for propagation speed and direction , and for the synchronization of the gait phases .
This may be particularly advantageous whenever the computational costs of stochastic simulation may only be justified when a final system design is considered , but would be prohibitive when exploring many design options and conditions during early phases of design . The analysis of the threshold for the self-organisation of crowds in a city is an example of such explorative analysis that would require many simulations to obtain an accurate view of the phase-transition . This is even more so when more complex city-topologies and a large number of agents are considered .
Pedestrian detection in crowded scenes is an intractable problem in computer vision , in which occlusion often presents a great challenge . In this paper , we propose a novel context-aware feature learning method for detecting pedestrians in crowds , with the purpose of making better use of context information for dealing with occlusion . Unlike most current pedestrian detectors that only extract context information from a single and fixed region , a new pixel-level context embedding module is developed to integrate multi-cue context into a deep CNN feature hierarchy , which enables access to the context of various regions by multi-branch convolution layers with different receptive fields .
This paper focus on the research on visual technology of the 3D crowds and emphasizes how to decrease the feature loss of model , then presents a model simplification method to realize crowds rendering based on LOD .
We evaluate our approach over millions of queries from a commercial search engine ( i.e. , Baidu Map ) . Experimental results show that the proposed approach provides significant performance improvements over the competitive existing methods in terms of classical metrics ( i.e. , Recall@K and MRR ) as well as the prediction of crowds ’ search missions .
With agent-based method then each virtual agents can have different behavior and abilities . Optimal reciprocal collision avoidance(ORCA ) which is the best version of Reciprocal velocity obstacle(RVO ) used as virtual agents collision avoidance in crowds simulation . Experiment result demonstrates the simulation of hundreds of agents in a three evacuation scenario .
This overlay network will provide scalable and balanced access to the students and teachers to e-learning facilities . The CCDN will be composed by caches used as edge delivery nodes , content routing capabilities to route the client to the closest copy of the content , distributed content distribution and management and improving the access of users in terms of speed , reliability , scalability and prevention of flash-crowds . We present as a case study the work in progress of the deployment of a CCDN in the technical university of Valencia together with an e-learning streaming application over the CCDN .
Doppler Shift Measurement Using Complex-Valued CSI of WiFi in Corridors Curvelet transform sub-difference image for crowd estimation Mask-Aware Networks for Crowd Counting A hybrid model of convolutional neural networks and deep regression forests for crowd counting Revisiting Perspective Information for Efficient Crowd Counting Perspective-Aware CNN For Crowd Counting Machine and Deep Learning for Crowd Analytics A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation Measuring the Accuracy of Crowd Counting using Wi-Fi Probe-Request-Frame Counting Technique Multi-Stream Networks and Ground-Truth Generation for Crowd Counting HSBS: A Human's Heat Signature and Background Subtraction Hybrid Approach for Crowd Counting and Analysis Stacked Pooling: Improving Crowd Counting by Boosting Scale Invariance Positional averaging explains crowding with letter-like stimuli Modeling, Simulation and Visual Analysis of Crowds: A Multidisciplinary Perspective Modeling, Simulation and Visual Analysis of Crowds Crowd Counting and Profiling: Methodology and Evaluation Crowd behaviour and congestion analysis through deep machine learning A Data‐Driven Framework for Visual Crowd Analysis Crowd Counting in Low-Resolution Crowded Scenes Using Region-Based Deep Convolutional Neural Networks Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds Sketch and test: visual crowd research using p5.js Transferring Learned Microcalcification Group Detection from 2D Mammography to 3D Digital Breast Tomosynthesis Using a Hierarchical Model and Scope-based Normalization Features Social Groups Detection in Crowd through Shape-Augmented Structured Learning Group emotion modelling and the use of middleware for virtual crowds in video-games Local Segmentation for Pedestrian Tracking in Dense Crowds One for All? Managing External and Internal Crowds Through a Single Platform - A Case Study. Social flocks: a crowd simulation framework for social network generation, community detection, and collective behavior modeling Analysis and Design of the Dynamical Stability of Collective Behavior in Crowds A combined process algebraic, agent and fluid flow approach to emergent crowd behaviour Learning Pixel-Level and Instance-Level Context-Aware Features for Pedestrian Detection in Crowds A LOD Simplification Method of 3D Crowds Rendering Hierarchical Contextual Attention Recurrent Neural Network for Map Query Suggestion Crowds Evacuation Simulation on Heterogeneous Agent Using Agent-Based Reciprocal Velocity Obstacle CCDN: campus content delivery network learning facility
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2018 2014 2018 2020 2018 2018 2019 2017 2016 2020 2016 2018 2009 2013 2013 2013 2019 2014 2019 2018 2020 2016 2013 2010 2014 2017 2011 2011 2010 2019 2013 2017 2018 2003
ICCSCE Modeling, Simulation and Visual Analysis of Crowds Modeling, Simulation and Visual Analysis of Crowds Modeling, Simulation and Visual Analysis of Crowds ECCV (2) ICIAP (1) MMM (1) HICSS KDD ICALT
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