• 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.

• Journal of the Society of Disaster Information
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• v.7 no.1
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• pp.12-20
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• 2011

This study was performed for identifying the characteristics of mass gathering and human injury in mass gathering based on the literature survey and analysis of mass gathering and crowd packing. The size and density of crowd influenced the characteristics of mass gathering according to type of mass gathering. The variables and causes of human injuries of mass gathering have positive or negative influences based on the weather, attendance, duration, location, mobility, event type, crowd mood, alcohol, drug, crowd density and age. Based on the physical mechanism of crowd packing, the degree of crowd packing was influenced by crowd pressure, crowd density and lasting time. But the magnitude of pressure for pedestrian injury criteria remains for further research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2974-2992
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• 2021

Crowd-based outsourcing is an emerging trend in testing, which integrates advantages of crowd-based outsourcing in software testing. Open call format is used to accomplish various network tasks involving different types of testing levels and techniques at various places by software testers. Crowd-sourced software testing can lead to an improper testing process as if it does not allocate the right task to the right crowd with required skills and not choose the right crowd; it can lead to huge results, which become time-consuming and challenging crowd-source manager for the identification of improper one. The primary purpose of this research is to make crowd-based outsourced software testing more effective and reliable by relating association between the software tester, personality characteristic, and different levels of software testing, i.e., unit, integration, and system, in order to find appropriate personality characteristic for required testing level. This research has shown an observed experiment to determine which software testing level suits which personality characteristic tester in a crowd-based software testing environment. A total of 1000 software testers from different software houses and firms in Pakistan were registered to perform tasks at different software testing levels. The Myers-Briggs Type Indicator (MBTI) test is used to identify each tester's personality characteristic involved in this research study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4355-4374
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• 2018

Crowd escape event detection has become one of the hottest problems in intelligent surveillance filed. When the 'escape event' occurs, pedestrians will escape in a disordered way with different velocities and directions. Based on these characteristics, this paper proposes a Direction-Collectiveness Model to detect escape event in crowd scenes. First, we extract a set of trajectories from video sequences by using generalized Kanade-Lucas-Tomasi key point tracker (gKLT). Second, a Direction-Collectiveness Model is built based on the randomness of velocity and orientation calculated from the trajectories to express the movement of the crowd. This model can describe the movement of the crowd adequately. To obtain a generalized crowd escape event detector, we adopt an adaptive threshold according to the Direction-Collectiveness index. Experiments conducted on two widely used datasets demonstrate that the proposed model can detect the escape events more effectively from dense crowd.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.1
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• pp.97-105
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• 2014

This study attempts to review the theories and related literatures on crowd funding and build the framework to understand the core concepts of crowd funding. In addition, I explore the applicability of crowd funding platforms for start-ups. For this purpose, I analyze the framework based on the life cycles of projects on crowd funding with the components of stake-holders. Furthermore, I review the results of previous outstanding literature on pros and cons of crowd funding to identify the applicability of crowd funding for start-ups. The results show that the use of crowd funding platform alleviates the risks involved with the information goods which occupy major portion of the projects listed on crowd funding have unique cost structure with high fixed cost and very low variable cost. Consequently, crowd funding is identified as a platform for start-ups of information goods by leveraging the wisdom of crowd. However, crowd funding may not work as an effective platform for start-ups if group thinking emerges. The study provides an implication that it provides a framework for understanding crowd funding and its applicability for start-ups, which can support government's policy for creative economy. Additional empirical research analyzing the data on the projects listed on crowd funding platforms in Korea will be performed in the succeeding study.

• Journal of Information Technology Services
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• v.13 no.4
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• pp.123-137
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• 2014

Since the global financial crisis, funding for startups or ventures has been diminished seriously. In this situation, crowd-funding has attracted worldwide attention as a viable means to financing startups/ventures. After reviewing existing laws in other countries, this study investigated the current status of crowd-funding legalization in Korea and surveyed requirements from three groups of participants to crowd-funding. Investors already recognized the high risk of crowd-funding into startups and asked for the protection of their investment. But, their priorities were on the enlargement of tax benefits and the establishment of a trading market for selling their equity. In addition, investors wanted to shorten the compulsory holding period. Issuers as fund raisers hoped fast legalization, convenient process of crowd-funding, and expansion of government support. Crowd-funding platform operators requested policies for establishing paths to become new growth companies from startups, tax benefits, and the setup of separate fund for startups. Currently, the National Assembly in Korea is holding the discussion on the submitted law. For desirable outcomes to all participants, it would be better to proceed in the following sequence. First, legalize the crowd-funding with clauses protecting investors; Second, encourage investment through tax benefits; Third, develop communities among participants for reducing information asymmetry; And fourth, establish diverse trading markets for selling equities of startups.

• 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.

• Journal of Korean Society of Steel Construction
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• v.24 no.1
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• pp.1-11
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• 2012

To overcome the limitations of current evaluation procedures for floor vibration under crowd loading, two kinds of uncertainties associated with individual time lag differences and the complex behavior of crowd should be taken into account. The complex behavior of crowds has yet to be fully described, even though individual differences can be dealt with statistically. This paper proposes time lag considered (TLC) crowd model based on a probabilistic approach. The load reduction factor, which reflects the effect of a general degree of synchronization among crowd, is proposed. Extensive Monte Carlo simulations were carried out to determine various crowd behaviors by using the TLC crowd model proposed. The TLC crowd model can rationally treat the energy loss of various crowd patterns. This indicates that it may be used as a theoretical basis in refining dynamic load factor of crowd loading.

• Journal of Korea Multimedia Society
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• v.17 no.9
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• pp.1104-1114
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• 2014

In large-scale crowd simulations, it is very important for the decision-making system of manipulating interactive behaviors to minimize the computational cost for controlling realistic behaviors such as collision avoidance. In this paper, we propose a large-scale realtime crowd simulation method using the affordance and navigation potential fields such as attractive and repulsive forces of electromagnetic fields. In particular, the model that we propose locally handles the realistic interactions between agents, and thus radically reduces the cost of expensive computation on interactions which has been the most problematic in crowd simulation. Our method is widely applicable to the expression and analysis of various crowd behaviors that are needed in behavior control in computer games, crowd scenes in movies, emergent behaviors of evacuation, etc.

• Industrial Engineering and Management Systems
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• v.14 no.4
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• pp.325-334
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• 2015

Nowadays, crowdsourcing has become a popular concept and is widely used in many social, economical and technological areas. For consumer product design, crowdsourcing is implemented extensively with many cases. Although there has been a lot of research on the application of crowdsourcing for product design, the big picture of how factors influence the participartion of individuals from the crowd in a crowdsourcing project for product design has not yet been understood. This paper aims to investigate the relationships of crowd participation and influencing factors including: process, product, and reward. To do this, we conducted a survey on a crowd of engineering individuals and analyzed the collected data with data mining techniques. Main findings include the relationships of crowd participation versus process, product, and reward factors as well as regression models to predict crowd participation.