• Title/Summary/Keyword: Real-time Segmentation

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Real-Time Pig Segmentation for Individual Pig Monitoring in a Weaning Pig Room (이유자돈사에서 개별 돼지 모니터링을 위한 실시간 돼지 구분)

  • Ju, Miso;Baek, Hansol;Sa, Jaewon;Kim, Heegon;Chung, Yongwha;Park, Daihee
    • Journal of Korea Multimedia Society
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    • v.19 no.2
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    • pp.215-223
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    • 2016
  • To reduce huge losses in pig farms, weaning pigs with weak immune systems are required to be carefully supervised. Even if various researches have been performed for livestock monitoring environment, segmenting each pig from touching pigs is still entrenched as a difficult problem. In this paper, we propose a real-time segmentation method for moving pigs by using motion information in a 24-h video surveillance system. The experimental results with the videos obtained from a domestic pig farm illustrated the possibility for segmenting by using our proposed method in real-time.

Head Position Detection Using Omnidirectional Camera (전 방향 카메라 영상에서 사람의 얼굴 위치검출 방법)

  • Bae, Kwang-Hyuk;Park, Kang-Ryoung;Kim, Jai-Hie
    • Proceedings of the IEEK Conference
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    • 2007.07a
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    • pp.283-284
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    • 2007
  • This paper proposes a method of real-time segmentation of moving region and detection of head position in a single omnidrectional camera Segmentation of moving region used background modeling method by a mixture of Gaussian(MOG) and shadow detection method. Circular constraint was proposed for detecting head position.

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Maritime Object Segmentation and Tracking by using Radar and Visual Camera Integration

  • Hwang, Jae-Jeong;Cho, Sang-Gyu;Lee, Jung-Sik;Park, Sang-Hyon
    • Journal of information and communication convergence engineering
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    • v.8 no.4
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    • pp.466-471
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    • 2010
  • We have proposed a method to detect and track moving ships using position from Radar and image processor. Real-time segmentation of moving regions in image sequences is a fundamental step in the radar-camera integrated system. Algorithms for segmentation of objects are implemented by composing of background subtraction, morphologic operation, connected components labeling, region growing, and minimum enclosing rectangle. Once the moving objects are detected, tracking is only performed upon pixels labeled as foreground with reduced additional computational burdens.

Super-Pixel-Based Segmentation and Classification for UAV Image (슈퍼 픽셀기반 무인항공 영상 영역분할 및 분류)

  • Kim, In-Kyu;Hwang, Seung-Jun;Na, Jong-Pil;Park, Seung-Je;Baek, Joong-Hwan
    • Journal of Advanced Navigation Technology
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    • v.18 no.2
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    • pp.151-157
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    • 2014
  • Recently UAV(unmanned aerial vehicle) is frequently used not only for military purpose but also for civil purpose. UAV automatically navigates following the coordinates input in advance using GPS information. However it is impossible when GPS cannot be received because of jamming or external interference. In order to solve this problem, we propose a real-time segmentation and classification algorithm for the specific regions from UAV image in this paper. We use the super-pixels algorithm using graph-based image segmentation as a pre-processing stage for the feature extraction. We choose the most ideal model by analyzing various color models and mixture color models. Also, we use support vector machine for classification, which is one of the machine learning algorithms and can use small quantity of training data. 18 color and texture feature vectors are extracted from the UAV image, then 3 classes of regions; river, vinyl house, rice filed are classified in real-time through training and prediction processes.

Real-time Segmentation of Black Ice Region in Infrared Road Images

  • Li, Yu-Jie;Kang, Sun-Kyoung;Jung, Sung-Tae
    • Journal of the Korea Society of Computer and Information
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    • v.27 no.2
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    • pp.33-42
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    • 2022
  • In this paper, we proposed a deep learning model based on multi-scale dilated convolution feature fusion for the segmentation of black ice region in road image to send black ice warning to drivers in real time. In the proposed multi-scale dilated convolution feature fusion network, different dilated ratio convolutions are connected in parallel in the encoder blocks, and different dilated ratios are used in different resolution feature maps, and multi-layer feature information are fused together. The multi-scale dilated convolution feature fusion improves the performance by diversifying and expending the receptive field of the network and by preserving detailed space information and enhancing the effectiveness of diated convolutions. The performance of the proposed network model was gradually improved with the increase of the number of dilated convolution branch. The mIoU value of the proposed method is 96.46%, which was higher than the existing networks such as U-Net, FCN, PSPNet, ENet, LinkNet. The parameter was 1,858K, which was 6 times smaller than the existing LinkNet model. From the experimental results of Jetson Nano, the FPS of the proposed method was 3.63, which can realize segmentation of black ice field in real time.