Learning behavior patterns from video for agent-based crowd modeling and simulation

This paper proposes a novel data-driven modeling framework to construct agent-based crowd model based on real-world video data. The constructed crowd model can generate crowd behaviors that match those observed in the video and can be used to predict trajectories of pedestrians in the same scenario. In the proposed framework, a dual-layer architecture is proposed to model crowd behaviors. The bottom layer models the microscopic collision avoidance behaviors, while the top layer models the macroscopic crowd behaviors such as the goal selection patterns and the path navigation patterns. An automatic learning algorithm is proposed to learn behavior patterns from video data. The learned behavior patterns are then integrated into the dual-layer architecture to generate realistic crowd behaviors. To validate its effectiveness, the proposed framework is applied to two different real world scenarios. The simulation results demonstrate that the proposed framework can generate crowd behaviors similar to those observed in the videos in terms of crowd density distribution. In addition, the proposed framework can also offer promising performance on predicting the trajectories of pedestrians.     

A modeling framework for urban traffic systems microscopic simulation

In this paper a modeling framework for urban traffic systems (UTS) is presented. The model, used for agent based micro-simulation, describes both the traffic network and dynamic entities, namely vehicles, traffic lights, and pedestrians. The framework allows defining systematically the necessary components and their behavior of a model oriented to event driven simulation, which can be executed in a distributed way. In the model, the vehicles are conceived as mobile agents with decision making capabilities that interact with the environment and other entities within the traffic network, performing diverse activities according to numerous situations arisen during the simulation. A multi-level Petri net based formalism, named n-LNS is used for describing the structure of the UTS and the components behavior. The first level describes the traffic network; the second level models the behavior of diverse road network users considered as agents, and the third level specifies detailed procedures performed by the agents, namely travel plans, tasks, etc.     

A review of recent advances in learner and skill modeling in intelligent learning environments

In recent years, learner models have emerged from the research laboratory and research classrooms into the wider world. Learner models are now embedded in real world applications which can claim to have thousands, or even hundreds of thousands, of users. Probabilistic models for skill assessment are playing a key role in these advanced learning environments. In this paper, we review the learner models that have played the largest roles in the success of these learning environments, and also the latest advances in the modeling and assessment of learner skills. We conclude by discussing related advancements in modeling other key constructs such as learner motivation, emotional and attentional state, meta-cognition and self-regulated learning, group learning, and the recent movement towards open and shared learner models.     

Cuckoo search: recent advances and applications

Cuckoo search (CS) is a relatively new algorithm, developed by Yang and Deb in 2009, and the same has been found to be efficient in solving global optimization problems. In this paper, we review the fundamental ideas of cuckoo search and the latest developments as well as its applications. We analyze the algorithm and gain insight into its search mechanisms and find out why it is efficient. We also discuss the essence of algorithms and its link to self-organizing systems, and finally, we propose some important topics for further research.     

Modeling group structures in pedestrian crowd simulation

Grouping is a common phenomenon in pedestrian crowds and plays important roles in affecting crowd behavior. Group modeling is still an open challenging problem and has not been incorporated by existing crowd simulation models. Motivated by the need of group modeling for crowd behavior simulation, this paper presents a unified and well-defined framework for modeling the structure aspect of different groups in pedestrian crowds. Both intra-group structure and inter-group relationships are considered and their effects on the crowd behavior are modeled. Based on this framework, an agent-based crowd simulation system is developed and crowd behavior simulations using two different group structures are presented. The simulation results show that the developed framework allows different group structures to be easily modeled. Besides, different group sizes, intra-group structures and inter-group relationships can have significant impacts on crowd behaviors.     

Industrial geometry: recent advances and applications in CAD

Industrial Geometry aims at unifying existing and developing new methods and algorithms for a variety of application areas with a strong geometric component. These include CAD, CAM, Geometric Modelling, Robotics, Computer Vision and Image Processing, Computer Graphics and Scientific Visualization. In this paper, Industrial Geometry is illustrated via the fruitful interplay of the areas indicated above in the context of novel solutions of CAD related, geometric optimization problems involving distance functions: approximation with general B-spline curves and surfaces or with subdivision surfaces, approximation with special surfaces for applications in architecture or manufacturing, approximate conversion from implicit to parametric (NURBS) representation, and registration problems for industrial inspection and 3D model generation from measurement data. Moreover, we describe a ‘feature sensitive’ metric on surfaces, whose definition relies on the concept of an image manifold, introduced into Computer Vision and Image Processing by Kimmel, Malladi and Sochen. This metric is sensitive to features such as smoothed edges, which are characterized by a significant deviation of the two principal curvatures. We illustrate its applications at hand of feature sensitive curve design on surfaces and local neighborhood definition and region growing as an aid in the segmentation process for reverse engineering of geometric objects.     

An integration of enhanced social force and crowd control models for high-density crowd simulation

Neural Computing and Applications - Social force model is one of the well-known approaches that can successfully simulate pedestrians’ movements realistically. However, it is not suitable to...     

ACMICS: an agent communication model for interacting crowd simulation

Behavioral plausibility is one of the major aims of crowd simulation research. We present a novel approach that simulates communication between the agents and assess its influence on overall crowd behavior. Our formulation uses a communication model that tends to simulate human-like communication capability. The underlying formulation is based on a message structure that corresponds to a simplified version of Foundation for Intelligent Physical Agents Agent Communication Language Message Structure Specification. Our algorithm distinguishes between low- and high-level communication tasks so that ACMICS can be easily extended and employed in new simulation scenarios. We highlight the performance of our communication model on different crowd simulation scenarios. We also extend our approach to model evacuation behavior in unknown environments. Overall, our communication model has a small runtime overhead and can be used for interactive simulation with tens or hundreds of agents.     

Cartilage proteomics: Challenges, solutions and recent advances

The mammalian skeleton is largely composed of cartilage and bone. The major functions of cartilage are first to provide a transient template for development of the axial and appendicular skeleton and secondly to provide permanent articulating joint surfaces. The unique cartilage extracellular matrix (ECM) is essential for the load-bearing and viscoelastic properties of cartilage tissues. Maintained by the chondrocytes, the ECM contains a myriad of proteins and proteoglycans organized into precise networks. Many cartilage disorders result from genetic disruption of cartilage ECM components, their interactions and/or degradation. Although technically challenging, the proteomic analysis of cartilage in development and disease is now emerging as a clinically important research area. In this article, we will review progress in the proteomic characterization of cartilage-related samples.     

Some recent advances in automated analysis

Due to the increasing complexity of software systems, there is a growing need for automated and scalable software synthesis and analysis. In the last decade, active research in the formal methods community brought interesting results and valuable tools. However, there are still challenges to face and hard problems that need to be solved. We briefly outline some recent trends, and review some of the latest achievements, introducing six papers selected from the 20th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2014).     

A survey on information visualization: recent advances and challenges

Information visualization (InfoVis), the study of transforming data, information, and knowledge into interactive visual representations, is very important to users because it provides mental models of information. The boom in big data analytics has triggered broad use of InfoVis in a variety of domains, ranging from finance to sports to politics. In this paper, we present a comprehensive survey and key insights into this fast-rising area. The research on InfoVis is organized into a taxonomy that contains four main categories, namely empirical methodologies, user interactions, visualization frameworks, and applications, which are each described in terms of their major goals, fundamental principles, recent trends, and state-of-the-art approaches. At the conclusion of this survey, we identify existing technical challenges and propose directions for future research.     

Beyond stack smashing: recent advances in exploiting buffer overruns

Security vulnerabilities related to buffer overruns account for the largest share of CERT advisories, as well as high-profile worms - from the original Internet Worm in 1987 through Blaster's appearance in 2003. When malicious crackers discover a vulnerability, they devise exploits that take advantage of the vulnerability to attack a system. The article describes three powerful general-purpose families of exploits for buffer overruns: arc injection, pointer subterfuge, and heap smashing. These new techniques go beyond the traditional "stack smashing" attack and invalidate traditional assumptions about buffer overruns.     

Shadow obstacle model for realistic corner-turning behavior in crowd simulation

This paper describes a novel model known as the shadow obstacle model to generate a realistic corner-turning behavior in crowd simulation. The motivation for this model comes from the observation that people tend to choose a safer route rather than a shorter one when turning a corner. To calculate a safer route, an optimization method is proposed to generate the corner-turning rule that maximizes the viewing range for the agents. By combining psychological and physical forces together, a full crowd simulation framework is established to provide a more realistic crowd simulation. We demonstrate that our model produces a more realistic corner-turning behavior by comparison with real data obtained from the experiments. Finally, we perform parameter analysis to show the believability of our model through a series of experiments.     

Model-based testing of software and systems: recent advances and challenges

Model-based testing is focused on testing techniques which rely on the use of models. The diversity of systems and software to be tested implies the need for research on a variety of models and methods for test automation. We briefly review this research area and introduce several papers selected from the 22nd International Conference on Testing Software and Systems (ICTSS).     

Scalable nonlinear dynamical systems for agent steering and crowd simulation

We present a new methodology for agent modeling that is scalable and efficient. It is based on the integration of nonlinear dynamical systems and kinetic data structures. The method consists of three layers, which together model 3D agent steering, crowds and flocks among moving and static obstacles. The first layer, the local layer employs nonlinear dynamical systems theory to models low-level behaviors. It is fast and efficient, and it does not depend on the total number of agents in the environment. This dynamical systems-based approach also allows us to establish continuous numerical parameters for modifying each agent's behavior. The second layer, a global environment layer consists of a specifically designed kinetic data structure to track efficiently the immediate environment of each agent and know which obstacles/agents are near or visible to the given agent. This layer reduces the complexity in the local layer. In the third layer, a global planning layer, the problem of target tracking is generalized in a way that allows navigation in maze-like terrains, avoidance of local minima and cooperation between agents. We implement this layer based on two approaches that are suitable for different applications: One approach is to track the closest single moving or static target; the second is to use a pre-specified vector field, which may be generated automatically (with harmonic functions, for example) or based on user input to achieve the desired output. We also discuss how hybrid systems concepts for global planning can capitalize on both our layered approach and the continuous, reactive nature of our agent steering.

We demonstrate the power of the approach through a series of experiments simulating single/multiple agents and crowds moving towards moving/static targets in complex environments.