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Tens of millions of people visit every year the Grand Mosque in Makkah for Hajj and Umrah. Making Tawaf (circumambulating the Ka’ba) is an essential rite in Hajj and Umrah. During peak hours, crowd density in Mataf (the area around the Ka’ba) becomes extremely intense. Even when the crowd density in Mataf is less, kissing the Black Stone (a part of the ritual of making the Tawaf) is a daunting task. This is due to the lack of proper queueing of pilgrims wishing to touch and kiss the Black Stone. In this work, we propose a Finite State Machine (FSM) based model for simulating the crowd movement during Tawaf. The model can be implemented in real time crowd monitoring in Mataf. Based on the models outputs, a system for auto-activated barriers is proposed to arrange the pilgrims in queue who want to touch and kiss the Black Stone.

Crowd counting using wireless sensing relies on either participatory or non-participatory methods. In both cases, the algorithm developed would predict the number of people or their density within the area monitored, for the purpose of crowd safety and management. An alternative to such approaches is the path loss modeling. A model is proposed to count the number of people by utilizing non-participatory (non-intrusive) technique supported by Design of Experiments (DOE) statistical method. The DOE method would identify the significant crowd properties that would inflict major signal losses and thus formulate the model. Results show that the proposed model has better accuracy compared to the other related works.

In this work, we propose an unsupervised approach for detecting the anomalies in a crowd scene using social network model. Using a window-based approach, scene objects are first detected and tracked, and a spatio-temporal partitioning is constructed to produce a set of spatio-temporal cuboids that capture spatial and temporal features. A hierarchical social network is built to model the crowd behavior: the bottom-level models local behavior and the top level models the global. We perform anomaly detection and demonstrate the effectiveness of the proposed approach on a benchmark crowd analysis video sequences. Our results reveal that we outperform majority, if not all, the state-of-the-art methods.

Mismanagement in traditional market has made its existence was harmed by modern market. Traditional market is identified as a dirty, crowded, and uncomfortable place to shop. This condition is very different with the condition in modern market. One of the challenges is making the traditional market become a comfort place to shop. The research about crowd modeling in traditional market is being done. One of the partial researches in this main research is designing traditional market customers' behavior. Multi agents system has been chosen as a platform because it has been chosen in many crowd modeling researches. This paper is a report of this research. This research process is divided into steps. The first step is reviewing literatures which have correlation with the research topic. The second step is doing field observation. The field observation has been done in one of traditional market in Yogyakarta. The third step is designing the customers' behavior based on the literatures and field observation. The fourth step is developing simulation based on the design. From the simulation, it can be found that there is potential where persons can't pass through the corridor because the corridor is fully loaded. Some solutions are proposed to avoid that situation.

Agent-based evacuation modeling approach is gained more and more attention for investigating human cognitive capabilities and social behaviors in building fires. This paper mainly overviews the research about various agent-based evacuation models. For the decision-making reflects the intelligence of agent individual, we define three types of behavior decision-making models for agent individual based on the collected literatures and sum up the characteristics of each type. Aiming at crowd' important influence on evacuation processes, we also sum up two types of agent-based crowd modeling approaches to simulate crowd evacuation processes and give corresponding cases. Finally, after summing up the existing problems, the outlooks of the research from computaional experiments point of view is proposed.

Analyzing human crowds is an important issue in video surveillance and is a challenging task due to their nature of non-rigid shapes. In this paper, optical flows are first estimated and then used for a clue to cluster human crowds into groups in unsupervised manner using our proposed clustering method. While the clusters of human crowds are obtained, their behaviors with attributes, orientation, position and crowd size, are characterized by a model of force field. Finally, we can predict the behaviors of human crowds based on the model and then detect if any anomalies of human crowd(s) present in the scene. Experiment results obtained by using extensive dataset show that our system is effective and efficient in detect anomalous events for uncontrolled environment of surveillance videos.

To support the evacuation process of crowds from emergency situations, we have developed a wearable device, LifeBelt, for vibro tactile guidance of individuals in panic towards exits. Since visual and auditory perception is overwhelmed in situations of panic, exit sign and loudspeaker based evacuation often appears ineffective. LifeBelt as a coordinated navigation device builds on a subtle directionalvibration stimulation to navigate and guide individuals to escape.This paper compares the effectiveness of a LifeBelt supported vs. non-supported evacuation from spaces with multiple exits. Based on an extended Moore’s model of neighborhood for next step behavior, and a predicted shortest time to escape metric, we investigate on the efficiency effect of (i) individuals following the nearest exit (without LifeBelt), and (ii) individuals adheringLifeBelt recommendations towards the earliest escape exit. Large scale simulations show that even in situations of growing panic, LifeBelt improves evacuation efficiency by more than 34,5 %.

Video surveillance in crowd is challenging for public security. This paper focuses on the detection of human abnormal behaviors in crowd. The issue is crucial in some special localities and has been less studied. To achieve this goal, this paper defines a crowd energy based on Markov Random Fields. By using wavelet analysis of the energy curves, the crowd status of the scene is detected. After testing the method in the actual environment in a metro surveillance system, we have obtained a result which shows that the method can be used to deal with crowd modeling and real-time surveillance satisfactorily.

This paper presents a basic multi-layer model of human movement process. A behavior model is used to simulate actions of individual pedestrians while a cellular automata model is used to simulate their small scale movements. A modular platform, which implements the proposed model, is also presented. The platform is being used to simulate different sections of the Muslim holy mosque in Makkah. Preliminary results of the simulation of circular pedestrian movements in a section of the mosque along with the algorithm used are reported.

Surveillance on pedestrians or other moving objects in crowded sites is a crucial and challenging problem for various security tasks. To solve this problem, this paper addresses two energy methods which are based on intensity variation and motion features respectively. With wavelet analysis of the energy curves, the result shows that both methods can be used to deal with crowd modeling and real-time surveillance satisfactorily. A comparison of the two methods is then made in the real environment in a metro surveillance system.