Tae-Ki An

A New Diverse AdaBoost Classifier

AdaBoost is one of the most popular algorithms to construct a strong classifier with linear combination of member classifiers. The member classifiers are selected to minimize the errors in each iteration step during training process. AdaBoost provides very simple and useful method to generate ensemble classifiers. The performance of the ensemble depends on the diversity among the member classifiers as well as the performance of each member classifiers. However the existing AdaBoost algorithms are focused on error minimization problems. In this paper, we propose a noble method to inject diversity into the AdaBoost process to improve the performance of the AdaBoost classifiers. The proposed Diverse AdaBoost algorithm outperforms Gentle AdaBoost algorithm, because of the injected diversity. Our research contributes to the method designing optimized ensemble classifiers with diversity.

Estimation of Crowd Density in Public Areas Based on Neural Network

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.

Measurement of the Crowd Density in Outdoor Using Neural Network

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.

Railroad Information Integrated-Service and Its Knowledge-Base Construction Method based on Passengers Needs Analysis

The railroad is one of the most popular means of transport. Howerver, railroad agencies are managing the railroad information and provide its service respectively. That is, because of managing the different information and its type, The user will not receive the correct and integrated information. In this study, We figure out the needs of passengers that they want in railroad service and seek an integrated service plan that railroad passengers can use the railroad information service at a time, at a glance and in one place. Also, we investigate the knowledge-base construction to provide a knowledge map service to railroad passengers.