• Proceedings of the Korea Information Processing Society Conference
• /
• 2009.04a
• /
• pp.173-176
• /
• 2009

본 논문에서는 감시시스템이 갖추어진 환경 내에서 발생할 수 있는 특이 행동을 효율적으로 감지하기 위한 기법을 제시한다. 최근 대형 범죄 및 방화 사건 등의 방지목적으로 DVR 의 단순 녹화를 벗어나 지능형 감시시스템을 도입하려는 연구가 활발히 진행되고 있다. 그러나 이러한 시스템들은 아직 초기 연구 단계에 있으며 영상내의 관심물체 추출을 위한 전경과 배경의 분리 및 추적 단계에 그치고 있다. 이에 본 논문에서는 가우시안 혼합 모델을 통하여 전경과 배경을 분리하고, 관심영역에 한해서 Optical Flow 기법을 이용하여 폭력상황과 같은 특이 행동의 감지 여부를 판단 할 수 있는 방법에 대해 실험을 통해 평가하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.10a
• /
• pp.83-86
• /
• 1997

우리는 자연계에서 새나 어류가 무리지어서 다니는 특이한 모습을 볼 수 있다. 본 논문을 복수 에이전트 모빌 로봇을 이용하여 이들이 효율적인 전략적 규칙으로부터 이런 복잡한 행동의 결과를 나타낼 수 있음을 보여준다. 모의 실험된 무리는 분산된 행동 모델로 구현되었으며 각각의 모빌 로봇간의 상대적으로 단순한 상호작용의 결과이다. 또한 여기서 모의 실험된 각각의 모빌 로봇은 동적인 환경을 감지함에 따라 움직이는 독립된 개체로서 자신의 움직임을 결정한다.

• Journal of Korea Multimedia Society
• /
• v.11 no.11
• /
• pp.1491-1500
• /
• 2008

This paper proposes an intelligent surveillance system to recognize suspicious patterns of the human behavior by using the Hidden Markov Model. First, the method finds foot area of the human by motion detection algorithm from image sequence of the surveillance camera. Then, these foot locus form observation series of features to learn the HMM. The feature that is position of the human foot is changed to each code that corresponds to a specific label among 16 local partitions of image region. Therefore, specific moving patterns formed by the foot locus are the series of the label numbers. The Baum-Welch algorithm of the HMM learns each suspicious and specific pattern to classify the human behaviors. To recognize the inputted human behavior pattern in a test image, the probabilistic comparison between the learned pattern of the HMM and foot series to be tested decides the categorization of the test pattern. The experimental results show that the method can be applied to detect a suspicious person prowling in corridor.

• Korean journal of applied entomology
• /
• v.61 no.1
• /
• pp.1-14
• /
• 2022

Moths have a well-developed sex pheromone communication system. Male moths exhibit an extremely sensitive and selective sex pheromone detection system so that they can detect the sex pheromone produced by conspecific females and locate them for successful mating. Using the pheromone detection system, male moths display characteristic stereotypic behavioral responses, flying upwind to follow intermittent filamentous pheromone strands in pheromone plume. The chemical composition of female sex pheromone in moths, typically comprised of multiple compounds, is species-specific. Male moths contain specialized pheromone receptor neurons on the antennae to detect conspecific sex pheromone accurately, and distinguish it from the pheromones produced by other species. The signals from pheromone receptor neurons are integrated and induce relevant behavior from the male moths. Male moths also contain olfactory sensory neurons in pheromone sensilla, specialized for pheromone-related behavioral antagonist compounds, which can enhance discrimination between conspecific and heterospecific pheromones. Here we review reports on the sex pheromone detection system in male moths and their related responses, and suggest future research direction.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.6
• /
• pp.1065-1071
• /
• 2023

In this paper discuss with research developing an LSTM model for classifying the behavior of occupants within a residence. The multi-sensor consists of an IAQ (Indoor Air Quality) sensor that measures indoor air quality, a UWB radar that tracks occupancy detection and location, and a Piezo sensor to measure occupants' biometric information, and collects occupant behavior data such as going out, staying, cooking, cleaning, exercise, and sleep by constructed an experimental environment similar to the actual residential environment. After the data with removed outliers and missing, the LSTM model is used to calculate accuracy, sensitivity, specificity of the occupant behavior classification model, T1 score.