The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
권호 표지
DOI QR코드

DOI QR Code

Smart Camera Technology to Support High Speed Video Processing in Vehicular Network

차량 네트워크에서 고속 영상처리 기반 스마트 카메라 기술

  • Son, Sanghyun (Pusan National University Dept. of Electrical and Computer Engineering) ;
  • Kim, Taewook (Pusan National University Dept. of Electrical and Computer Engineering) ;
  • Jeon, Yongsu (Pusan National University Dept. of Electrical and Computer Engineering) ;
  • Baek, Yunju (Pusan National University Dept. of Electrical and Computer Engineering)
  • Received : 2014.09.30
  • Accepted : 2015.01.08
  • Published : 2015.01.30
Download PDF
⟨ Previous Next ⟩

Abstract

A rapid development of semiconductors, sensors and mobile network technologies has enable that the embedded device includes high sensitivity sensors, wireless communication modules and a video processing module for vehicular environment, and many researchers have been actively studying the smart car technology combined on the high performance embedded devices. The vehicle is increased as the development of society, and the risk of accidents is increasing gradually. Thus, the advanced driver assistance system providing the vehicular status and the surrounding environment of the vehicle to the driver using various sensor data is actively studied. In this paper, we design and implement the smart vehicular camera device providing the V2X communication and gathering environment information. And we studied the method to create the metadata from a received video data and sensor data using video analysis algorithm. In addition, we invent S-ROI, D-ROI methods that set a region of interest in a video frame to improve calculation performance. We performed the performance evaluation for two ROI methods. As the result, we confirmed the video processing speed that S-ROI is 3.0 times and D-ROI is 4.8 times better than a full frame analysis.

최근 반도체 기술, 센서 기술 및 이동통신 기술의 발전으로 스마트 자동차 기술 연구 개발이 진행 중에 있다. 사회가 발전함에 따라 차량이 증가하였고 사고에 대한 위험은 점차 높아지고 있다. 그에 따라 기존의 차량용 블랙박스 외에 차량의 각종 센서 정보를 활용하여 운전자에게 다양한 정보를 제공하는 첨단 운전자 보조 시스템이 연구되고 있다. 본 논문에서는 차량 간의 통신기능을 포함하고, 주변의 정보를 습득하여 제공할 수 있는 스마트 카메라 장치를 설계 및 구현하여, 장치에 포함된 카메라로부터 입력 받은 영상을 분석하여 획득한 정보를 영상 메타데이터화 하는 기술에 대한 연구를 수행하였다. 또한 임베디드 장치의 제한된 계산 성능을 보완하기 위해 관심영역을 설정하는 S-ROI(Static-Region Of Interest), D-ROI(Dynamic-Region Of Interest) 방식을 고안하였다. 실험을 통해 영상처리 속도가 전체영상 분석에 비해 S-ROI의 경우 3.0배, D-ROI의 경우 4.8배 향상함을 확인하였다.

Keywords

References

  1. H. Seo, J. Jung, and S. Lee, "Scenario and network performance evaluation for a do not pass warning service based on vehicle-tovehicle communications," J. KICS, vol. 38C, no. 03, pp. 227-232, Mar. 2013. https://doi.org/10.7840/kics.2013.38C.3.227
  2. S. Gil and G.-H. Kim, "Vision-based vehicle detection and tracking using online learning," J. KICS, vol. 39A, no. 01, pp. 1-11, Jan. 2014. https://doi.org/10.7840/kics.2014.39A.1.1
  3. Mobileye, Retrived Sept. 30, 2014, from http://www.mobileye.com
  4. IEEE Std 802.11p, IEEE Standard for information technology-telecommunications and information exchange between systemslocal and metropolitan area networks-specific requirements, Part 11, Amendent 6: Wireless Access in Vehicular Environments, 2010.
  5. IEEE Std 1609.3, IEEE Standard for Wireless Access in Vehicular Environments(WAVE) -Networking Services, 1-144, 2010.
  6. IEEE Std 1609.4, IEEE Standard for Wireless Access in Vehicular Environments(WAVE) -Multi-channel operation, 1-90, 2010.
  7. N. Dalai and B. Triggs, "Histograms of oriented gradients for human detection," IEEE Comput. Soc. Conf. Comput. Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886-893, San Diego, CA, USA, Jun. 2005.
  8. D. Geronimo, A. M. Lopez, A. D. Sappa, and T. Graf, "Survey of pedestrian detection for advanced driver assistance systems," IEEE Trans. Pattern Anal. Machine Intell., vol. 32, no. 7, pp. 1239-1258, 2010. https://doi.org/10.1109/TPAMI.2009.122
  9. H. Hartenstein and K. P. Laberteaux, "A Tutorial Survey on Vehicular Ad Hoc Networks," IEEE Commun. Mag., pp. 164-171, 2008.
  10. C. Sommer and F. Dressler, "The DYMO routing protocol in VANET scenarios," IEEE Veh. Technol. Conf. (VTC-2007), pp. 16-20, Baltimore, MD, Sept.-Oct. 2007.
  11. Y.-S. Chen, Y.-W. Lin, and S.-L. Lee, "A mobicast routing protocol in vehicular ad-hoc networks," in Proc. ACM/Springer Mob. Netw. Appl., vol. 15, no. 1, pp. 20-35, Feb. 2010. https://doi.org/10.1007/s11036-009-0176-3
  12. V. Namboodiri and L. Gao, "Prediction-based routing for vehicular ad hoc networks," IEEE Trans. Veh. Technol., vol. 56, no. 4, pp. 2332-2345, Jul. 2007. https://doi.org/10.1109/TVT.2007.897656
  13. M. Al-Rabayah and R. Malaney, "A new scalable hybrid routing protocol for VANETs," IEEE Trans. Veh. Technol., vol. 61, no. 6, pp. 2625-2635, 2012. https://doi.org/10.1109/TVT.2012.2198837
  14. J. Jeong, S. Guo, Y. Gu, T. He, and D. H. C. Du, "Trajectory-based statistical forwarding for multihop infrastructure-to-vehicle data delivery," IEEE Trans. Mob. Comput., vol. 11, no. 10, pp. 1523-1537, 2012. https://doi.org/10.1109/TMC.2011.189
  15. S. Cho and S. Kim, "Routing algorithm of VANET for an efficient path management in urban intersections," J. KICS, vol. 38A, no. 12, pp. 1054-1060, Dec. 2013. https://doi.org/10.7840/kics.2013.38A.12.1054
  16. D. Borsetti and J. Gozalvez, "Infrastructureassisted geo-routing for cooperative vehicular networks," in Proc. IEEE Veh. Netw. Conf., pp. 255-262, Jersey City, NJ, Dec. 2010.
  17. OpenCV, Retrieved Sept. 30, 2014, from http://opencv.org
  18. Linuxwireless, Retrieved Nov. 13, 2014, from http://wireless.kernel.org
  19. ODROID-XU (HardKernel), Retrieved Nov. 13, 2014, from http://www.hardkernel.com/main/products/prdt_info.php?g_code=G137510300620
  20. Wi-Fi Alliance, "Wi-Fi peer-to-peer(P2P) technical specification 1.1,"
  21. B. Kim, S. Son, T. Kim, and Y. Baek, "Design and implementation of a vehicular network platform using Wi-Fi P2P," in Proc. KICS Int. Conf. Commun. 2010 (KICS ICC 2014), pp. 89-90, Jeju Island, Korea, Jun. 2014.
  22. Exynos 4412 Prime Quad Core CPU Module, Retrieved Nov. 13, 2014, from http://www.hardkernel.com/main/products/prdt_info.php?g_code=G135270682824
  23. Arndale Octa, Retrieved Nov. 13, 2014, from http://www.arndaleboard.org/wiki/index.php/Main_Page

Cited by

  1. Performance Improvement of Lane Detector Using Grouping Method vol.16, pp.7, 2018, https://doi.org/10.14801/jkiit.2018.16.7.51
  2. 병렬 분산 처리를 이용한 영상 기반 실내 위치인식 시스템의 프레임워크 구현 vol.41, pp.11, 2016, https://doi.org/10.7840/kics.2016.41.11.1490
  3. OBD 차량 진단 정보를 위한 IoT 장치 구현 vol.41, pp.12, 2015, https://doi.org/10.7840/kics.2016.41.12.1851
  4. 관심 영역 기반의 자동 노출 조절 알고리즘을 적용한 다중 노출 차량용 스마트 카메라의 설계 및 구현 vol.42, pp.1, 2017, https://doi.org/10.7840/kics.2017.42.1.181
  5. 차량용 블랙박스 영상파일의 무결성 검증에 해시함수 이용 방법 vol.42, pp.1, 2015, https://doi.org/10.7840/kics.2017.42.1.241
  6. 신경회로망 기반의 주야간 안개 감지 알고리즘 vol.42, pp.3, 2015, https://doi.org/10.7840/kics.2017.42.3.673