• Title/Summary/Keyword: video information extraction

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On-Road Car Detection System Using VD-GMM 2.0 (차량검출 GMM 2.0을 적용한 도로 위의 차량 검출 시스템 구축)

  • Lee, Okmin;Won, Insu;Lee, Sangmin;Kwon, Jangwoo
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.40 no.11
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    • pp.2291-2297
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    • 2015
  • This paper presents a vehicle detection system using the video as a input image what has moving of vehicles.. Input image has constraints. it has to get fixed view and downward view obliquely from top of the road. Road detection is required to use only the road area in the input image. In introduction, we suggest the experiment result and the critical point of motion history image extraction method, SIFT(Scale_Invariant Feature Transform) algorithm and histogram analysis to detect vehicles. To solve these problem, we propose using applied Gaussian Mixture Model(GMM) that is the Vehicle Detection GMM(VDGMM). In addition, we optimize VDGMM to detect vehicles more and named VDGMM 2.0. In result of experiment, each precision, recall and F1 rate is 9%, 53%, 15% for GMM without road detection and 85%, 77%, 80% for VDGMM2.0 with road detection.

A Study on an Open/Closed Eye Detection Algorithm for Drowsy Driver Detection (운전자 졸음 검출을 위한 눈 개폐 검출 알고리즘 연구)

  • Kim, TaeHyeong;Lim, Woong;Sim, Donggyu
    • Journal of the Institute of Electronics and Information Engineers
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    • v.53 no.7
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    • pp.67-77
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    • 2016
  • In this paper, we propose an algorithm for open/closed eye detection based on modified Hausdorff distance. The proposed algorithm consists of two parts, face detection and open/closed eye detection parts. To detect faces in an image, MCT (Modified Census Transform) is employed based on characteristics of the local structure which uses relative pixel values in the area with fixed size. Then, the coordinates of eyes are found and open/closed eyes are detected using MHD (Modified Hausdorff Distance) in the detected face region. Firstly, face detection process creates an MCT image in terms of various face images and extract criteria features by PCA(Principle Component Analysis) on offline. After extraction of criteria features, it detects a face region via the process which compares features newly extracted from the input face image and criteria features by using Euclidean distance. Afterward, the process finds out the coordinates of eyes and detects open/closed eye using template matching based on MHD in each eye region. In performance evaluation, the proposed algorithm achieved 94.04% accuracy in average for open/closed eye detection in terms of test video sequences of gray scale with 30FPS/$320{\times}180$ resolution.

Development of CCTV Cooperation Tracking System for Real-Time Crime Monitoring (실시간 범죄 모니터링을 위한 CCTV 협업 추적시스템 개발 연구)

  • Choi, Woo-Chul;Na, Joon-Yeop
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.20 no.12
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    • pp.546-554
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    • 2019
  • Typically, closed-circuit television (CCTV) monitoring is mainly used for post-processes (i.e. to provide evidence after an incident has occurred), but by using a streaming video feed, machine-based learning, and advanced image recognition techniques, current technology can be extended to respond to crimes or reports of missing persons in real time. The multi-CCTV cooperation technique developed in this study is a program model that delivers similarity information about a suspect (or moving object) extracted via CCTV at one location and sent to a monitoring agent to track the selected suspect or object when he, she, or it moves out of range to another CCTV camera. To improve the operating efficiency of local government CCTV control centers, we describe here the partial automation of a CCTV control system that currently relies upon monitoring by human agents. We envisage an integrated crime prevention service, which incorporates the cooperative CCTV network suggested in this study and that can easily be experienced by citizens in ways such as determining a precise individual location in real time and providing a crime prevention service linked to smartphones and/or crime prevention/safety information.

Watermarking for Digital Hologram by a Deep Neural Network and its Training Considering the Hologram Data Characteristics (딥 뉴럴 네트워크에 의한 디지털 홀로그램의 워터마킹 및 홀로그램 데이터 특성을 고려한 학습)

  • Lee, Juwon;Lee, Jae-Eun;Seo, Young-Ho;Kim, Dong-Wook
    • Journal of Broadcast Engineering
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    • v.26 no.3
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    • pp.296-307
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    • 2021
  • A digital hologram (DH) is an ultra-high value-added video content that includes 3D information in 2D data. Therefore, its intellectual property rights must be protected for its distribution. For this, this paper proposes a watermarking method of DH using a deep neural network. This method is a watermark (WM) invisibility, attack robustness, and blind watermarking method that does not use host information in WM extraction. The proposed network consists of four sub-networks: pre-processing for each of the host and WM, WM embedding watermark, and WM extracting watermark. This network expand the WM data to the host instead of shrinking host data to WM and concatenate it to the host to insert the WM by considering the characteristics of a DH having a strong high frequency component. In addition, in the training of this network, the difference in performance according to the data distribution property of DH is identified, and a method of selecting a training data set with the best performance in all types of DH is presented. The proposed method is tested for various types and strengths of attacks to show its performance. It also shows that this method has high practicality as it operates independently of the resolution of the host DH and WM data.

An Emulation System for Efficient Verification of ASIC Design (ASIC 설계의 효과적인 검증을 위한 에뮬레이션 시스템)

  • 유광기;정정화
    • Journal of the Korean Institute of Telematics and Electronics C
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    • v.36C no.10
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    • pp.17-28
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    • 1999
  • In this paper, an ASIC emulation system called ACE (ASIC Emulator) is proposed. It can produce the prototype of target ASIC in a short time and verify the function of ASIC circuit immediately The ACE is consist of emulation software in which there are EDIF reader, library translator, technology mapper, circuit partitioner and LDF generator and emulation hardware including emulation board and logic analyzer. Technology mapping is consist of three steps such as circuit partitioning and extraction of logic function, minimization of logic function and grouping of logic function. During those procedures, the number of basic logic blocks and maximum levels are minimized by making the output to be assigned in a same block sharing product-terms and input variables as much as possible. Circuit partitioner obtain chip-level netlists satisfying some constraints on routing structure of emulation board as well as the architecture of FPGA chip. A new partitioning algorithm whose objective function is the minimization of the number of interconnections among FPGA chips and among group of FPGA chips is proposed. The routing structure of emulation board take the advantage of complete graph and partial crossbar structure in order to minimize the interconnection delay between FPGA chips regardless of circuit size. logic analyzer display the waveform of probing signal on PC monitor that is designated by user. In order to evaluate the performance of the proposed emulation system, video Quad-splitter, one of the commercial ASIC, is implemented on the emulation board. Experimental results show that it is operated in the real time of 14.3MHz and functioned perfectly.

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