Skip to main content
Top

2018 | OriginalPaper | Chapter

Intrusion Detection for High-Speed Railway Perimeter Obstacle

Authors : Qinghong Liu, Yong Qin, Zhengyu Xie, Tangwen Yang, Gaoyun An

Published in: Proceedings of the 3rd International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2017

Publisher: Springer Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Perimeter intrusion detection is one of the most important conditions for high-speed railway safety. The research and application of perimeter intrusion detection are first introduced, including an infrared detector, pulse electronic fence, vibration cable/fiber optic cable, intelligent video analysis, etc. Then, we analyze the application of video surveillance in perimeter intrusion detection, point out the difficulties of video surveillance for the day- and nighttime, and summarize the popular methods of the intruding object recognition and the intruding behavior analysis. Finally, we put forward the future research direction of perimeter intrusion detection for high-speed railway.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Institute of computing technologies of China academy of railway sciences, “High-speed railway perimeter intrusion alarm system technology implementation plan (2016)” Institute of computing technologies of China academy of railway sciences, “High-speed railway perimeter intrusion alarm system technology implementation plan (2016)”
2.
go back to reference Ma H (2014) Research on the technologies of distributed intelligent monitoring for invasion limit of foreign body along the railway based on machine vision. Lanzhou Jiaotong University, Lan Zhou (in Chinese) Ma H (2014) Research on the technologies of distributed intelligent monitoring for invasion limit of foreign body along the railway based on machine vision. Lanzhou Jiaotong University, Lan Zhou (in Chinese)
3.
go back to reference Lou R (2006) Study on video surveillance technology for railway roadblock. Southwest Jiaotong University, Chengdu (in Chinese) Lou R (2006) Study on video surveillance technology for railway roadblock. Southwest Jiaotong University, Chengdu (in Chinese)
4.
go back to reference García JJ, Ureña J, Hernández Á et al (2010) Efficient multisensory barrier for obstacle detection on railways. IEEE Trans Intell Transp Syst 11(3):702–713CrossRef García JJ, Ureña J, Hernández Á et al (2010) Efficient multisensory barrier for obstacle detection on railways. IEEE Trans Intell Transp Syst 11(3):702–713CrossRef
5.
go back to reference Šilar Z, Dobrovolný M (2013) The obstacle detection on the railway crossing based on optical flow and clustering. In: Telecommunications and signal processing, 2013 36th international conference, Rome, IEEE, 755–759 Šilar Z, Dobrovolný M (2013) The obstacle detection on the railway crossing based on optical flow and clustering. In: Telecommunications and signal processing, 2013 36th international conference, Rome, IEEE, 755–759
6.
go back to reference Catalano A, Bruno FA, Galliano C, Pisco M, Persiano GV, Cutolo A, Cusano A (2017) An optical fiber intrusion detection system for railway security. Sens Actuators A: Phys 253:91–100CrossRef Catalano A, Bruno FA, Galliano C, Pisco M, Persiano GV, Cutolo A, Cusano A (2017) An optical fiber intrusion detection system for railway security. Sens Actuators A: Phys 253:91–100CrossRef
7.
go back to reference Uribe JA, Fonseca L, Vargas JF (2012) Video based system for railroad collision warning. In: Security technology (ICCST), 2012 IEEE international Carnahan conference on, IEEE, pp 280–285 Uribe JA, Fonseca L, Vargas JF (2012) Video based system for railroad collision warning. In: Security technology (ICCST), 2012 IEEE international Carnahan conference on, IEEE, pp 280–285
8.
go back to reference Wang T, Shi H et al (2009) Research on intrusion monitoring system for PDL. Railway Comput App 18(7):8–10 (in Chinese) Wang T, Shi H et al (2009) Research on intrusion monitoring system for PDL. Railway Comput App 18(7):8–10 (in Chinese)
9.
go back to reference Li L (2013) Study on fiber bragg grating sensors for slop network safety monitoring. Wuhan University of Technology, Wuan, pp 7–14 (in Chinese) Li L (2013) Study on fiber bragg grating sensors for slop network safety monitoring. Wuhan University of Technology, Wuan, pp 7–14 (in Chinese)
10.
go back to reference Lei T, Zhu L et al (2012) Railway obstacle detection using onboard forward-viewing camera. J Transp Syst Eng Inf Technol 12(04):79–83 + 134 (in Chinese) Lei T, Zhu L et al (2012) Railway obstacle detection using onboard forward-viewing camera. J Transp Syst Eng Inf Technol 12(04):79–83 + 134 (in Chinese)
11.
go back to reference Shi H, Chai H et al (2015) Study on railway embedded detection algorithm for railway intrusion based on object recognition and tracking. J China Railway Society. 37(07):58–65 (in Chinese) Shi H, Chai H et al (2015) Study on railway embedded detection algorithm for railway intrusion based on object recognition and tracking. J China Railway Society. 37(07):58–65 (in Chinese)
12.
go back to reference Wang Y, Yu Z et al (2016) Hardware implementation of detection algorithm for railway clearance based on FPGA. J China Railway Soc 38(03):84–91 (in Chinese) Wang Y, Yu Z et al (2016) Hardware implementation of detection algorithm for railway clearance based on FPGA. J China Railway Soc 38(03):84–91 (in Chinese)
13.
go back to reference Wang Q, Liang X et al (2014) Visual detection method for the invasion of slowly changing foreign matters to railway lines. China Railway Sci 35(3):137–143 (in Chinese) Wang Q, Liang X et al (2014) Visual detection method for the invasion of slowly changing foreign matters to railway lines. China Railway Sci 35(3):137–143 (in Chinese)
14.
go back to reference Hou D, Sun X et al (2014) Stimulation non-uniformity in induction thermography and its separating method. Chin J Sci Instrum 35(07):1466–1475 (in Chinese) Hou D, Sun X et al (2014) Stimulation non-uniformity in induction thermography and its separating method. Chin J Sci Instrum 35(07):1466–1475 (in Chinese)
15.
go back to reference Yu J, Xu D et al (2011) Image defogging: a survey. J Image Graph, 16(9):1561–1576 (in Chinese) Yu J, Xu D et al (2011) Image defogging: a survey. J Image Graph, 16(9):1561–1576 (in Chinese)
16.
go back to reference He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353 He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353
17.
go back to reference Wang Y, Yin C et al (2014) Image haze removal using a bilateral filter. J Image Graph 19(3):387–392 (in Chinese) Wang Y, Yin C et al (2014) Image haze removal using a bilateral filter. J Image Graph 19(3):387–392 (in Chinese)
18.
go back to reference Zhang J, Wu X et al (2013) Improved algorithm for image dehazing using dark channel prior. Video Eng 37(23):192–195 (in Chinese) Zhang J, Wu X et al (2013) Improved algorithm for image dehazing using dark channel prior. Video Eng 37(23):192–195 (in Chinese)
19.
go back to reference Ting S (2015) The image defogging algorithm based on physics and nonphysics model. Jishou University (in Chinese) Ting S (2015) The image defogging algorithm based on physics and nonphysics model. Jishou University (in Chinese)
20.
go back to reference Krishnan S, Venkataraman D (2012) Restoration of video by removing rain. Int J Comput Sci Eng App 2(2):19–28 Krishnan S, Venkataraman D (2012) Restoration of video by removing rain. Int J Comput Sci Eng App 2(2):19–28
21.
go back to reference Fu YH, Kang LW, Lin CW et al (2011) Single-frame-based rain removal via image decomposition. In: Proceeding of 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE Press, Prague, Czech, pp 1453–1456 Fu YH, Kang LW, Lin CW et al (2011) Single-frame-based rain removal via image decomposition. In: Proceeding of 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE Press, Prague, Czech, pp 1453–1456
22.
go back to reference Kang LW, Lin CW, Fu YH (2012) Automatic single-image-based rain streaks removal via image decomposition. IEEE Trans Image Process 21(4):1742–1755MathSciNetCrossRefMATH Kang LW, Lin CW, Fu YH (2012) Automatic single-image-based rain streaks removal via image decomposition. IEEE Trans Image Process 21(4):1742–1755MathSciNetCrossRefMATH
23.
go back to reference Jia Z, Wang H, Caballero RE et al (2012) A two-step approach to see-through bad weather for surveillance video quality enhancement. Mach Vis Appl 23(6):1059–1082CrossRef Jia Z, Wang H, Caballero RE et al (2012) A two-step approach to see-through bad weather for surveillance video quality enhancement. Mach Vis Appl 23(6):1059–1082CrossRef
24.
go back to reference Barnum PC, Narasimhan S, Kanade T (2010) Analysis of rain and snow in frequency space. Int J Comput Vision 86(2–3):256–274CrossRef Barnum PC, Narasimhan S, Kanade T (2010) Analysis of rain and snow in frequency space. Int J Comput Vision 86(2–3):256–274CrossRef
25.
26.
go back to reference Hase H, Miyake K, Yoneda M (2008) Real-time snowfall noise elimination. In: Proceedings of the 1999 IEEE international conference on image processing, Springer, Berlin, Heidelberg, pp 451–458 Hase H, Miyake K, Yoneda M (2008) Real-time snowfall noise elimination. In: Proceedings of the 1999 IEEE international conference on image processing, Springer, Berlin, Heidelberg, pp 451–458
27.
go back to reference Xu L (2007) Detection and removal of snow from videos. Xinjiang University (in Chinese) Xu L (2007) Detection and removal of snow from videos. Xinjiang University (in Chinese)
28.
go back to reference Pei SC, Tsai YT, Lee CY (2014) Removing rain and snow in a single image using saturation and visibility features. In: IEEE international conference on multimedia and expo workshops. IEEE, pp 1–6 Pei SC, Tsai YT, Lee CY (2014) Removing rain and snow in a single image using saturation and visibility features. In: IEEE international conference on multimedia and expo workshops. IEEE, pp 1–6
29.
go back to reference Sun Y, Duan X et al (2011) Research on removal algorithm of rain and snow from images based on improved snake model. Appl Res Comput 28(5):1991–1993 (in Chinese) Sun Y, Duan X et al (2011) Research on removal algorithm of rain and snow from images based on improved snake model. Appl Res Comput 28(5):1991–1993 (in Chinese)
30.
go back to reference Nan X (2016) Railway integrated video monitoring system technology and its linkage scheme. Jiangxi University of Finance and Economics (in Chinese) Nan X (2016) Railway integrated video monitoring system technology and its linkage scheme. Jiangxi University of Finance and Economics (in Chinese)
31.
go back to reference Li K, Feng J (2015) Discussion on the application of new technology of video monitoring in railway. Chin Railways 04:99–102 (in Chinese) Li K, Feng J (2015) Discussion on the application of new technology of video monitoring in railway. Chin Railways 04:99–102 (in Chinese)
32.
go back to reference Han L, Yu Y (2014) Application of thermal imaging technology in railway monitoring. Chin Railways 05:112–114 (in Chinese) Han L, Yu Y (2014) Application of thermal imaging technology in railway monitoring. Chin Railways 05:112–114 (in Chinese)
33.
go back to reference Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. Computer vision and pattern recognition, 2005. CVPR 2005. In: IEEE computer society conference on IEEE, 2005, 1:886–893. 2005, 1:886–893 Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. Computer vision and pattern recognition, 2005. CVPR 2005. In: IEEE computer society conference on IEEE, 2005, 1:886–893. 2005, 1:886–893
34.
go back to reference Ulhaq A, Yin X, He J, Zhang Y (2016) FACE: fully automated context enhancement for night-time video sequences. J Vis Commun Image Represent, Part B, October 2016, 40:682–693 Ulhaq A, Yin X, He J, Zhang Y (2016) FACE: fully automated context enhancement for night-time video sequences. J Vis Commun Image Represent, Part B, October 2016, 40:682–693
35.
go back to reference Chen L, Li W, Xu Z et al (2012) Pedestrian detection based on ISC in infrared images. Networking and distributed computing (ICNDC). In: 2012 third international conference on, IEEE, pp 166–169 Chen L, Li W, Xu Z et al (2012) Pedestrian detection based on ISC in infrared images. Networking and distributed computing (ICNDC). In: 2012 third international conference on, IEEE, pp 166–169
36.
go back to reference Felzenszwalb PF, Girshick RB, Mc Allester D et al (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645CrossRef Felzenszwalb PF, Girshick RB, Mc Allester D et al (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645CrossRef
37.
go back to reference Ren X, Ramanan D (2013) Histograms of sparse codes for object detection. Computer vision and pattern recognition (CVPR). In: 2013 IEEE conference on IEEE, pp 3246–3253 Ren X, Ramanan D (2013) Histograms of sparse codes for object detection. Computer vision and pattern recognition (CVPR). In: 2013 IEEE conference on IEEE, pp 3246–3253
38.
go back to reference Miron A, Basbes B, Rogozan A et al (2012) Intensity self-similarity features for pedestrian detection in far-infrared images. In: Intelligent vehicles symposium (IV), 2012 IEEE, pp 1120–1125 Miron A, Basbes B, Rogozan A et al (2012) Intensity self-similarity features for pedestrian detection in far-infrared images. In: Intelligent vehicles symposium (IV), 2012 IEEE, pp 1120–1125
39.
go back to reference Kim DS, Kim M, Kim BS et al (2013) Histograms of local intensity differences for pedestrian classification in far-infrared images. Electron Lett 49(4):258–260CrossRef Kim DS, Kim M, Kim BS et al (2013) Histograms of local intensity differences for pedestrian classification in far-infrared images. Electron Lett 49(4):258–260CrossRef
40.
go back to reference Hu Q, Wang L (2016) Pedestrian detection in infrared images based on multi-features. Electronic Des Eng 24(04):182–185 + 189 (in Chinese) Hu Q, Wang L (2016) Pedestrian detection in infrared images based on multi-features. Electronic Des Eng 24(04):182–185 + 189 (in Chinese)
41.
go back to reference Hu Q, Lv P (2016) Pedestrian detection in infrared images based on multi-features fusion. J Comput App 36(S1):157–160 + 195 (in Chinese) Hu Q, Lv P (2016) Pedestrian detection in infrared images based on multi-features fusion. J Comput App 36(S1):157–160 + 195 (in Chinese)
42.
go back to reference Luo Y, Wu TW, Hwang JN (2003) Object-based analysis and interpretation of human motion in sports video sequences by dynamic Bayesian networks. Comput Vis Image Underst 92(23):196–216CrossRef Luo Y, Wu TW, Hwang JN (2003) Object-based analysis and interpretation of human motion in sports video sequences by dynamic Bayesian networks. Comput Vis Image Underst 92(23):196–216CrossRef
43.
go back to reference Park S, Aggarwal JK (2004) A hierarchical Bayesian network for event recognition of human actions and interactions. ACM J Multimedia Syst Spec Issue Video Surveill 10(2):164–179CrossRef Park S, Aggarwal JK (2004) A hierarchical Bayesian network for event recognition of human actions and interactions. ACM J Multimedia Syst Spec Issue Video Surveill 10(2):164–179CrossRef
44.
go back to reference Buccolieri F, Distante C, Leone A (2005) Human posture recognition using active contours and radial basis function neural network. In: Proc of Conference on advanced video and signal based surveillance Buccolieri F, Distante C, Leone A (2005) Human posture recognition using active contours and radial basis function neural network. In: Proc of Conference on advanced video and signal based surveillance
45.
go back to reference Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. Adv Neural Inf Process Syst 1(4):568–576 Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. Adv Neural Inf Process Syst 1(4):568–576
46.
go back to reference Wang L, Xiong Y, Wang Z et al (2016) Temporal segment networks: towards good practices for deep action recognition. Acm Trans Info Syst 22(1):20–36 Wang L, Xiong Y, Wang Z et al (2016) Temporal segment networks: towards good practices for deep action recognition. Acm Trans Info Syst 22(1):20–36
47.
go back to reference Wu Z, Jiang YG, Wang X et al (2015) Fusing multi-stream deep networks for video classification. Comput Sci Wu Z, Jiang YG, Wang X et al (2015) Fusing multi-stream deep networks for video classification. Comput Sci
48.
go back to reference Sharma S, Kiros R, Salakhutdinov R (2015) Action recognition using visual attention. Comput Sci Sharma S, Kiros R, Salakhutdinov R (2015) Action recognition using visual attention. Comput Sci
49.
go back to reference Tran D, Bourdev L, Fergus R et al (2014) Learning spatiotemporal features with 3D convolutional networks, pp 4489–4497 Tran D, Bourdev L, Fergus R et al (2014) Learning spatiotemporal features with 3D convolutional networks, pp 4489–4497
50.
go back to reference Bilen H, Fernando B, Gavves E et al (2016) Dynamic image networks for action recognition. In: IEEE conference on computer vision and pattern recognition, IEEE Computer Society, pp 3034–3042 Bilen H, Fernando B, Gavves E et al (2016) Dynamic image networks for action recognition. In: IEEE conference on computer vision and pattern recognition, IEEE Computer Society, pp 3034–3042
Metadata
Title
Intrusion Detection for High-Speed Railway Perimeter Obstacle
Authors
Qinghong Liu
Yong Qin
Zhengyu Xie
Tangwen Yang
Gaoyun An
Copyright Year
2018
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-10-7989-4_47

Premium Partner