• Title/Summary/Keyword: multi-camera system

Search Result 473, Processing Time 0.02 seconds

Vehicle Detection and Tracking using Billboard Sweep Stereo Matching Algorithm (빌보드 스윕 스테레오 시차정합 알고리즘을 이용한 차량 검출 및 추적)

  • Park, Min Woo;Won, Kwang Hee;Jung, Soon Ki
    • Journal of Korea Multimedia Society
    • /
    • v.16 no.6
    • /
    • pp.764-781
    • /
    • 2013
  • In this paper, we propose a highly precise vehicle detection method with low false alarm using billboard sweep stereo matching and multi-stage hypothesis generation. First, we capture stereo images from cameras established in front of the vehicle and obtain the disparity map in which the regions of ground plane or background are removed using billboard sweep stereo matching algorithm. And then, we perform the vehicle detection and tracking on the labeled disparity map. The vehicle detection and tracking consists of three steps. In the learning step, the SVM(support vector machine) classifier is obtained using the features extracted from the gabor filter. The second step is the vehicle detection which performs the sobel edge detection in the image of the left camera and extracts candidates of the vehicle using edge image and billboard sweep stereo disparity map. The final step is the vehicle tracking using template matching in the next frame. Removal process of the tracking regions improves the system performance in the candidate region of the vehicle on the succeeding frames.

DEVELOPMENT OF THE MECHANICAL STRUCTURE OF THE MIRIS SOC (MIRIS 우주관측카메라의 기계부 개발)

  • Moon, B.K.;Jeong, W.S.;Cha, S.M.;Ree, C.H.;Park, S.J.;Lee, D.H.;Yuk, I.S.;Park, Y.S.;Park, J.H.;Nam, U.W.;Matsumoto, Toshio;Yoshida, Seiji;Yang, S.C.;Lee, S.H.;Rhee, S.W.;Han, W.
    • Publications of The Korean Astronomical Society
    • /
    • v.24 no.1
    • /
    • pp.53-64
    • /
    • 2009
  • MIRIS is the main payload of the STSAT-3 (Science and Technology Satellite 3) and the first infrared space telescope for astronomical observation in Korea. MIRIS space observation camera (SOC) covers the observation wavelength from $0.9{\mu}m$ to $2.0{\mu}m$ with a wide field of view $3.67^{\circ}\times3.67^{\circ}$. The PICNIC HgCdTe detector in a cold box is cooled down below 100K by a micro Stirling cooler of which cooling capacity is 220mW at 77K. MIRIS SOC adopts passive cooling technique to chill the telescope below 200 K by pointing to the deep space (3K). The cooling mechanism employs a radiator, a Winston cone baffle, a thermal shield, MLI (Multi Layer Insulation) of 30 layers, and GFRP (Glass Fiber Reinforced Plastic) pipe support in the system. Optomechanical analysis was made in order to estimate and compensate possible stresses from the thermal contraction of mounting parts at cryogenic temperatures. Finite Element Analysis (FEA) of mechanical structure was also conducted to ensure safety and stability in launching environments and in orbit. MIRIS SOC will mainly perform Galactic plane survey with narrow band filters (Pa $\alpha$ and Pa $\alpha$ continuum) and CIB (Cosmic Infrared Background) observation with wide band filters (I and H) driven by a cryogenic stepping motor.

Land Cover Classification of High-Spatial Resolution Imagery using Fixed-Wing UAV (고정익 UAV를 이용한 고해상도 영상의 토지피복분류)

  • Yang, Sung-Ryong;Lee, Hak-Sool
    • Journal of the Society of Disaster Information
    • /
    • v.14 no.4
    • /
    • pp.501-509
    • /
    • 2018
  • Purpose: UAV-based photo measurements are being researched using UAVs in the space information field as they are not only cost-effective compared to conventional aerial imaging but also easy to obtain high-resolution data on desired time and location. In this study, the UAV-based high-resolution images were used to perform the land cover classification. Method: RGB cameras were used to obtain high-resolution images, and in addition, multi-distribution cameras were used to photograph the same regions in order to accurately classify the feeding areas. Finally, Land cover classification was carried out for a total of seven classes using created ortho image by RGB and multispectral camera, DSM(Digital Surface Model), NDVI(Normalized Difference Vegetation Index), GLCM(Gray-Level Co-occurrence Matrix) using RF (Random Forest), a representative supervisory classification system. Results: To assess the accuracy of the classification, an accuracy assessment based on the error matrix was conducted, and the accuracy assessment results were verified that the proposed method could effectively classify classes in the region by comparing with the supervisory results using RGB images only. Conclusion: In case of adding orthoimage, multispectral image, NDVI and GLCM proposed in this study, accuracy was higher than that of conventional orthoimage. Future research will attempt to improve classification accuracy through the development of additional input data.