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Kwee-Bo Sim

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5 papers
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5

EAAI Journal 2018 Journal Article

Deep convolutional framework for abnormal behavior detection in a smart surveillance system

  • Kwang-Eun Ko
  • Kwee-Bo Sim

The ability to instantly detect risky behavior in video surveillance systems is a critical issue in a smart surveillance system. In this paper, a unified framework based on a deep convolutional framework is proposed to detect abnormal human behavior from a standard RGB image. The objective of the unified structure is to improve detection speed while maintaining recognition accuracy. The deep convolutional framework consists of (1) a human subject detection and discrimination module that is proposed to solve the problem of separating object entities, in contrast to previous object detection algorithms, (2) a posture classification module to extract spatial features of abnormal behavior, and (3) an abnormal behavior detection module based on long short-term memory (LSTM). Experiments on a benchmark dataset evaluate the potential of the proposed method in the context of smart surveillance. The results indicate that the proposed method provides satisfactory performance in detecting abnormal behavior in a real-world scenario.

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ICRA Conference 2001 Conference Paper

Artificial Immune-Based Swarm Behaviors of Distributed Autonomous Robotic Systems

  • Sang-Joon Sun
  • Dong-Wook Lee
  • Kwee-Bo Sim

We propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on the immune system in distributed autonomous robotic system (DARS). The immune system is a living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in a dynamically changing environment. For applying the immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy and its behavior strategy is stimulated and suppressed by other robots using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and the idiotopic network hypothesis. It is used for decision making of optimal swarm strategy. By T-cell modeling, adaptation ability of robot is enhanced in dynamic environments.

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IROS Conference 1999 Conference Paper

The fuzzy classifier system using the implicit bucket brigade algorithm

  • Chi-Sun Joung
  • Dong-Wook Lee
  • Kwee-Bo Sim

The fuzzy classifier system (FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. The classifier system can evaluate the usefulness of rules represented by classifiers with repeated learning It is the FCS that applies this ability of the machine learning to the concept of fuzzy controller. It is that the antecedent and consequent of classifier is same as a fuzzy rule of the rule base. In this paper, the FCS is the Michigan style and fuzzifies the input values to create the messages. The system stores those messages in the message list and uses the implicit bucket brigade algorithms. Also the FCS employs the genetic algorithms (GAs) to make new rules and modify rules when performance of the system needs to be improved.

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IROS Conference 1993 Conference Paper

On developing an adaptive neural-fuzzy control system

  • Seok Hyeon Kim
  • Y. -H. Kim
  • Kwee-Bo Sim
  • Hong-Tae Jeon

An adaptive neural-fuzzy control scheme for intelligent control is proposed. The control system consists of a fuzzy-neural controller (FNC) and model neural network (MNN). In the FNC, the antecedence and consequence of the fuzzy rule are constructed by a clustering method and a multilayer neural network. In the MNN, a multilayer neural network is utilized to identify an unknown controlled plant. The error backpropagation algorithm has been adopted as a learning technique. The effectiveness of the scheme is demonstrated by computer simulations of a cart-pole and a two-d. o. f. robot manipulator.

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