• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2170-2190
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• 2012

There are nowadays strong demands for intelligent surveillance systems, which can infer or understand more complex behavior. The application of crowd density estimation methods could lead to a better understanding of crowd behavior, improved design of the built environment, and increased pedestrian safety. In this paper, we propose a new crowd density estimation method, which aims at estimating not only a moving crowd, but also a stationary crowd, using images captured from surveillance cameras situated in various public locations. The crowd density of the moving people is measured, based on the moving area during a specified time period. The moving area is defined as the area where the magnitude of the accumulated optical flow exceeds a predefined threshold. In contrast, the stationary crowd density is estimated from the coarseness of textures, under the assumption that each person can be regarded as a textural unit. A multilayer neural network is designed, to classify crowd density levels into 5 classes. Finally, the proposed method is experimented with PETS 2009 and the platform of Gangnam subway station image sequences.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3769-3789
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• 2018

Social pedestrian groups are the basic elements that constitute a crowd; therefore, detection of such groups is scientifically important for modeling social behavior, as well as practically useful for crowd video understanding. A social group refers to a cluster of members who tend to keep similar motion state for a sustained period of time. One of the main challenges of social group detection arises from the complex dynamic variations of crowd patterns. Therefore, most works model dynamic groups to analysis the crowd behavior, ignoring the existence of stationary groups in crowd scene. However, in this paper, we propose a novel unified framework for detecting social pedestrian groups in crowd videos, including dynamic and stationary pedestrian groups, based on spatiotemporal-oriented energy measurements. Dynamic pedestrian groups are hierarchically clustered based on energy flow similarities and trajectory motion correlations between the atomic groups extracted from principal spatiotemporal-oriented energies. Furthermore, the probability distribution of static spatiotemporal-oriented energies is modeled to detect stationary pedestrian groups. Extensive experiments on challenging datasets demonstrate that our method can achieve superior results for social pedestrian group detection and crowd video classification.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.103-110
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• 2012

The population growth along with the urbanization, has caused more problems in many public areas, such as subway airport terminals, hospital, etc. Many surveillance systems have been installed in the public areas, but not all of those can be monitored in real-time, because the operators that observe the monitors are very small compared with the number of the monitors. For example, the observer can miss some crucial accidents or detect after considerable delays. Thus, intelligent surveillance system for preventing the accidents are needed, such as Intelligent Surveillance Systems. in this paper, we propose a new crowd density estimation method which aims at estimating moving crowd using images from surveillance cameras situated in outdoor locations. The moving crowd is estimated from the area where using optical flow. The edge information is also used as feature to measure the crowd density, so we improve the accuracy of estimation of crowd density. A multilayer neural network is designed to classify crowd density into 5 classes. Finally the proposed method is experimented with PETS 2009 images.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.415-420
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• 2020

Crowd flows occur in metropolitan railway transit stations, terminals, multiple buildings, and stadiums and are important in ensuring the safety as well as smooth flow of pedestrians in these facilities. In this study, the author developed a new computational analysis method for crowd flow dynamics and applied it to models of transit connecting paths. Using the analysis method, the potential value of the exit was assigned the smallest value, and the potential value of the surrounding grids gradually increased to form the overall potential map. A pathline map was then constructed by determining the direction vector from the grid with large potential value to the grid and small potential. These pathlines indicate basic routes of passenger flow. In all models of the analysis object, the pedestrians did not move to the first predicted shortest path but instead moved using alternative paths that changed depending on the situation. Even in bottlenecks in which pedestrians in both directions encountered each other, walking became much smoother if the entry time difference was dispersed. The results of the analysis show that a method for reducing congestion could be developed through software analysis such as passenger flow analysis without requiring hardware improvement work at the railway station.

• Journal of the Korean Society of Safety
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• v.33 no.6
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• pp.123-127
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• 2018

Recent catastrophic accidents at the underground subway stations in South Korea have proven that the subway evacuation is an important safety concern. Previous studies have used commercial programs for safety assessment or have been focused on development of computing algorithms rather than the basic analysis data which form the foundation of studies. In this study, we designed a new movement recording apparatus which measured and analyzed crowd movements including but not limited to moving velocity, specific flow rate and crowd density. Moreover, We propose new effective analysis method for evacuation studies with this apparatus.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.8
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• pp.731-737
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• 2014

This paper presents abnormal behavior detection in crowd within surveillance video. We have defined below two cases as a abnormal behavior; first as a sporadically spread phenomenon and second as a sudden running in same direction. In order to detect these two abnormal behaviors, we first extract the motion vector and propose a new descriptor which is combined MHOF(Multi-scale Histogram of Optical Flow) and DCHOF(Directional Change Histogram of Optical Flow). Also, binary classifier SVM(Support Vector Machine) is used for detection. The accuracy of the proposed algorithm is evaluated by both UMN and PETS 2009 dataset and comparisons with the state-of-the-art method validate the advantages of our algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2948-2963
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• 2015

In the field of computer vision, visual surveillance systems have recently become an important research topic. Growth in this area is being driven by both the increase in the availability of inexpensive computing devices and image sensors as well as the general inefficiency of manual surveillance and monitoring. In particular, the ultimate goal for many visual surveillance systems is to provide automatic activity recognition for events at a given site. A higher level of understanding of these activities requires certain lower-level computer vision tasks to be performed. So in this paper, we propose an intelligent activity recognition model that uses a structure learning method and a classification method. The structure learning method is provided as a K2-learning algorithm that generates Bayesian networks of causal relationships between sensors for a given activity. The statistical characteristics of the sensor values and the topological characteristics of the generated graphs are learned for each activity, and then a neural network is designed to classify the current activity according to the features extracted from the multiple sensor values that have been collected. Finally, the proposed method is implemented and tested by using PETS2013 benchmark data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3315-3337
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• 2012

Crowded scenes analysis is a challenging topic in computer vision field. How to detect diverse motion patterns in crowded scenarios from videos is the critical yet hard part of this problem. In this paper, we propose a novel approach to mining motion patterns by utilizing motion information during both long-term period and short interval simultaneously. To capture long-term motions effectively, we introduce Motion History Image (MHI) representation to access to the global perspective about the crowd motion. The combination of MHI and optical flow, which is used to get instant motion information, gives rise to discriminative spatial-temporal motion features. Benefitting from the robustness and efficiency of the novel motion representation, the following motion pattern mining is implemented in a completely unsupervised way. The motion vectors are clustered hierarchically through automatic hierarchical clustering algorithm building on the basis of graphic model. This method overcomes the instability of optical flow in dealing with time continuity in crowded scenes. The results of clustering reveal the situations of motion pattern distribution in current crowded videos. To validate the performance of the proposed approach, we conduct experimental evaluations on some challenging videos including vehicles and pedestrians. The reliable detection results demonstrate the effectiveness of our approach.

• Fire Science and Engineering
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• v.17 no.4
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• pp.1-6
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• 2003

This study measures the various moving velocities of elementary school children under situation of fire drill and suggests the methods of analysis. The velocities are such as the exiting velocity at the door of the classroom, personal walking velocity at corridor, velocity according to density of crowd and personal walking velocity at stairway. For these measurement an elementary school in Daejeon is chosen and 15 girls and 15 boys are selected in each grade. Finally speed data of the children is obtained and we can apply this data for the evacuation simulation of a school.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.4
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• pp.30-39
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• 2016

There have been many cases of deaths from crushing caused by dense crowds. Numerous studies about pedestrian flow have performed various simulations, but the experimental data to prove the simulations are still not enough. In this paper, the evacuation of pedestrians for proving pedestrian flow simulation is observed. Due to the possibility of real casualties, it is difficult to experiment with humans directly. Therefore, ten C57BL/6NCrSIc mice have been used. It is assumed that C57BL/6NCrSIc mice act like humans in panic situations. Electrical Stimulus Experiments on mice are conducted for exits with various angles. ICY software is applied in this paper. As a result, the mice escape fast at a proper angle of 45 to 60 degrees.