• Title/Summary/Keyword: Real-time Segmentation

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Video object segmentation and frame preprocessing for real-time and high compression MPEG-4 encoding (실시간 고압축 MPEG-4 부호화를 위한 비디오 객체 분할과 프레임 전처리)

  • 김준기;이호석
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.28 no.2C
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    • pp.147-161
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    • 2003
  • Video object segmentation is one of the core technologies for content-based real-time MPEG-4 encoding system. For real-time requirement, the segmentation algorithm should be fast and accurate but almost all existing algorithms are computationally intensive and not suitable for real-time applications. The MPEG-4 VM(Verification Model) has provided basic algorithms for MPEG-4 encoding but it has many limitations in practical software development, real-time camera input system and compression efficiency. In this paper, we implemented the preprocessing system for real-time camera input and VOP extraction for content-based video coding and also implemented motion detection to achieve the 180 : 1 compression rate for real-time and high compression MPEG-4 encoding.

A Study on the Implementation of the Picture segmentation for a Real-Time Automatic Video Tracker System (실시간 자동영상 추적기를 위한 영상영역화의 구현에 관한 연구)

  • 문종환;김경수;김재희
    • Proceedings of the Korean Institute of Communication Sciences Conference
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    • 1986.10a
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    • pp.186-190
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    • 1986
  • This paper describes a way of implementing the segmentation of 128*128 pixel images to be used as the inputs. to a real-time automatic video tracker. The suggested method uses the lowest valley-value of the computed intensity historgram with 16 levels. This method improves smoothing effects and also significantly reduces hardware requirements. Entire segmentation process is caried out in 10msec thus making a real time application possible.

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Digital Gray-Scale/Color Image-Segmentation Architecture for Cell-Network-Based Real-Time Applications

  • Koide, Tetsushi;Morimoto, Takashi;Harada, Youmei;Mattausch, Jurgen Hans
    • Proceedings of the IEEK Conference
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    • 2002.07a
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    • pp.670-673
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    • 2002
  • This paper proposes a digital algorithm for gray-scale/color image segmentation of real-time video signals and a cell-network-based implementation architecture in state-of-the-art CMOS technology. Through extrapolation of design and simulation results we predict that about 300$\times$300 pixels can be integrated on a chip at 100nm CMOS technology, realizing very high-speed segmentation at about 1600sec per color image. Consequently real-time color-video segmentation will become possible in near future.

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LiDAR based Real-time Ground Segmentation Algorithm for Autonomous Driving (자율주행을 위한 라이다 기반의 실시간 그라운드 세그멘테이션 알고리즘)

  • Lee, Ayoung;Yi, Kyongsu
    • Journal of Auto-vehicle Safety Association
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    • v.14 no.2
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    • pp.51-56
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    • 2022
  • This paper presents an Ground Segmentation algorithm to eliminate unnecessary Lidar Point Cloud Data (PCD) in an autonomous driving system. We consider Random Sample Consensus (Ransac) Algorithm to process lidar ground data. Ransac designates inlier and outlier to erase ground point cloud and classified PCD into two parts. Test results show removal of PCD from ground area by distinguishing inlier and outlier. The paper validates ground rejection algorithm in real time calculating the number of objects recognized by ground data compared to lidar raw data and ground segmented data based on the z-axis. Ground Segmentation is simulated by Robot Operating System (ROS) and an analysis of autonomous driving data is constructed by Matlab. The proposed algorithm can enhance performance of autonomous driving as misrecognizing circumstances are reduced.

Real-Time Object Segmentation in Image Sequences (연속 영상 기반 실시간 객체 분할)

  • Kang, Eui-Seon;Yoo, Seung-Hun
    • The KIPS Transactions:PartB
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    • v.18B no.4
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    • pp.173-180
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    • 2011
  • This paper shows an approach for real-time object segmentation on GPU (Graphics Processing Unit) using CUDA (Compute Unified Device Architecture). Recently, many applications that is monitoring system, motion analysis, object tracking or etc require real-time processing. It is not suitable for object segmentation to procedure real-time in CPU. NVIDIA provide CUDA platform for Parallel Processing for General Computation to upgrade limit of Hardware Graphic. In this paper, we use adaptive Gaussian Mixture Background Modeling in the step of object extraction and CCL(Connected Component Labeling) for classification. The speed of GPU and CPU is compared and evaluated with implementation in Core2 Quad processor with 2.4GHz.The GPU version achieved a speedup of 3x-4x over the CPU version.

A Study for a real-time variety region(object) extraction algorithm to implement MPEG-4 based Video Phones. (MPEG-4 기반의 영상전화기 구현을 위한 실시간 변환영역(객체) 추출에 관한 알고리즘)

  • Oh, In-Gwon;Shon, Young-Woo;Namgung, Jae-Chan
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.29 no.1C
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    • pp.92-101
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    • 2004
  • This paper proposes a algorithm to extract the variety region (object) from video for the real-time encoding of MPEG-4 based. The previous object segmentation methods cannot used the videophone or videoconference required by real-time processing. It is difficult to transfer a video to real-time because it increased complexity for the operation of each pixel on the spatial segmentation and temporal segmentation method proposed by MPEG-4 Working Group. But algorithm proposed for this thesis not operates a pixel unit but operates a macro block unit. Thus this enables real-time transfer. But this algorithm cannot extract several object for a image using proposed algorithm as previous algorithm. On system constructed by encoder and decoder. A proposed algorithm inserted for encoder as pre-process.

Black Ice Detection Platform and Its Evaluation using Jetson Nano Devices based on Convolutional Neural Network (CNN)

  • Sun-Kyoung KANG;Yeonwoo LEE
    • Korean Journal of Artificial Intelligence
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    • v.11 no.4
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    • pp.1-8
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    • 2023
  • In this paper, we propose a black ice detection platform framework using Convolutional Neural Networks (CNNs). To overcome black ice problem, we introduce a real-time based early warning platform using CNN-based architecture, and furthermore, in order to enhance the accuracy of black ice detection, we apply a multi-scale dilation convolution feature fusion (MsDC-FF) technique. Then, we establish a specialized experimental platform by using a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Experimental results of a real-time black ice detection platform show the better performance of our proposed network model compared to conventional image segmentation models. Our proposed platform have achieved real-time segmentation of road black ice areas by deploying a road black ice area segmentation network on the edge device Jetson Nano devices. This approach in parallel using multi-scale dilated convolutions with different dilation rates had faster segmentation speeds due to its smaller model parameters. The proposed MsCD-FF Net(2) model had the fastest segmentation speed at 5.53 frame per second (FPS). Thereby encouraging safe driving for motorists and providing decision support for road surface management in the road traffic monitoring department.

Implementation of Image Semantic Segmentation on Android Device using Deep Learning (딥-러닝을 활용한 안드로이드 플랫폼에서의 이미지 시맨틱 분할 구현)

  • Lee, Yong-Hwan;Kim, Youngseop
    • Journal of the Semiconductor & Display Technology
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    • v.19 no.2
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    • pp.88-91
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    • 2020
  • Image segmentation is the task of partitioning an image into multiple sets of pixels based on some characteristics. The objective is to simplify the image into a representation that is more meaningful and easier to analyze. In this paper, we apply deep-learning to pre-train the learning model, and implement an algorithm that performs image segmentation in real time by extracting frames for the stream input from the Android device. Based on the open source of DeepLab-v3+ implemented in Tensorflow, some convolution filters are modified to improve real-time operation on the Android platform.

A Study of Real-time Semantic Segmentation Performance Improvement in Unstructured Outdoor Environment (비정형 야지환경 주행상황에서의 실시간 의미론적 영상 분할 알고리즘 성능 향상에 관한 연구)

  • Daeyoung, Kim;Seunguk, Ahn;Seung-Woo, Seo
    • Journal of the Korea Institute of Military Science and Technology
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    • v.25 no.6
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    • pp.606-616
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    • 2022
  • Semantic segmentation in autonomous driving for unstructured environments is challenging due to the presence of uneven terrains, unstructured class boundaries, irregular features and strong textures. Current off-road datasets exhibit difficulties like class imbalance and understanding of varying environmental topography. To overcome these issues, we propose a deep learning framework for semantic segmentation that involves a pooled class semantic segmentation with five classes. The evaluation of the framework is carried out on two off-road driving datasets, RUGD and TAS500. The results show that our proposed method achieves high accuracy and real-time performance.

Realtime Human Object Segmentation Using Image and Skeleton Characteristics (영상 특성과 스켈레톤 분석을 이용한 실시간 인간 객체 추출)

  • Kim, Minjoon;Lee, Zucheul;Kim, Wonha
    • Journal of Broadcast Engineering
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    • v.21 no.5
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    • pp.782-791
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    • 2016
  • The object segmentation algorithm from the background could be used for object recognition and tracking, and many applications. To segment objects, this paper proposes a method that refer to several initial frames with real-time processing at fixed camera. First we suggest the probability model to segment object and background and we enhance the performance of algorithm analyzing the color consistency and focus characteristic of camera for several initial frames. We compensate the segmentation result by using human skeleton characteristic among extracted objects. Last the proposed method has the applicability for various mobile application as we minimize computing complexity for real-time video processing.