• Title/Summary/Keyword: Robust to Weather and Illumination Conditions

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Real-time Lane Violation Detection System using Feature Tracking (특징점 추적을 이용한 실시간 끼어들기 위반차량 검지 시스템)

  • Lee, Hee-Sin;Jeong, Sung-Hwan;Lee, Joon-Whoan
    • The KIPS Transactions:PartB
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    • v.18B no.4
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    • pp.201-212
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    • 2011
  • In this paper, we suggest a system of detecting a vehicle with lane violation, which can detect the vehicle with lane violation, by using the feature point tracking. The whole algorism in the suggested system of detecting a vehicle with lane violation is composed of three stages such as feature extraction, register and tracking in feature for the tracking-targeted vehicle, and detecting a vehicle with lane violation. The feature is extracted from the morphological gradient image, which results in constructing robust detection system against shadows, weather conditions, head lights and illumination conditions without distinction day and night. The system shows excellent performance for the data captured at day time, night time, and rainy night time as much as 99.49% for positive recognition ratio and 0.51% for error ratio. Also the system is so fast as much as 91.34 frames per second in average that it may be possible for real-time processing.

Fire Detection in Outdoor Using Statistical Characteristics of Smoke (연기의 통계적 특성을 이용한 실외 화재 감지)

  • Kim, Hyun-Tae;Park, Jang-Sik
    • The Journal of the Korea institute of electronic communication sciences
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    • v.9 no.2
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    • pp.149-154
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    • 2014
  • Detection performance of fire detection in the outdoor depends on weather conditions, the shadow by the movement of the sun, or illumination changes. In this paper, a smoke detection in conjunction with a robust background estimate algorithm to environment change in the outdoor in daytime is proposed. Gaussian Mixture Model (GMM) is applied as background estimation, and also, statistical characteristics of smoke is applied to detect the smoke for separated candidate region. Through the experiments with input videos obtained from a various weather conditions, the proposed algorithms were useful to detect smoke in the outdoor.

A Study for Video-based Vehicle Surveillance on Outdoor Road (실외 도로에서의 영상기반 차량 감시에 관한 연구)

  • Park, Keun-Soo;Kim, Hyun-Tae
    • The Journal of the Korea institute of electronic communication sciences
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    • v.8 no.11
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    • pp.1647-1654
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    • 2013
  • Detection performance of the vehicle on the road depends on weather conditions, the shadow by the movement of the sun, or illumination changes, etc. In this paper, a vehicle detection system in conjunction with a robust background estimate algorithm to environment change on the road in daytime is proposed. Gaussian Mixture Model is applied as background estimation algorithm, and also, Adaboost algorithm is applied to detect the vehicle for candidate region. Through the experiments with input videos obtained from a various weather conditions at the same actual road, the proposed algorithm were useful to detect vehicles in the road.