Intrusion Detection for High-Speed Railway Perimeter Obstacle

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.

[1]  YU Zhi-jing.  Research on removal algorithm of rain and snow from images based on improved snake model , 2011 .

[2]  Wang Hua-wei,et al.  Research on Intrusion Monitoring System for PDL , 2009 .

[3]  Wenshu Li,et al.  Pedestrian Detection Based on ISC in Infrared Images , 2012, 2012 Third International Conference on Networking and Distributed Computing.

[4]  D. Venkataraman,et al.  RESTORATION OF VIDEO BY REMOVING RAIN , 2012 .

[5]  Ziyou Xiong,et al.  A two-step approach to see-through bad weather for surveillance video quality enhancement , 2011, 2011 IEEE International Conference on Robotics and Automation.

[6]  Yanchun Zhang,et al.  FACE: Fully Automated Context Enhancement for night-time video sequences , 2016, J. Vis. Commun. Image Represent..

[7]  Shi Hong-me.  Study on Railway Embedded Detection Algorithm for Railway Intrusion Based on Object Recognition and Tracking , 2015 .

[8]  Lorenzo Torresani,et al.  Learning Spatiotemporal Features with 3D Convolutional Networks , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[9]  Andrew Zisserman,et al.  Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.

[10]  Alessandro Leone,et al.  Human posture recognition using active contours and radial basis function neural network , 2005, IEEE Conference on Advanced Video and Signal Based Surveillance, 2005..

[11]  Ruslan Salakhutdinov,et al.  Action Recognition using Visual Attention , 2015, NIPS 2015.

[12]  Takeo Kanade,et al.  Analysis of Rain and Snow in Frequency Space , 2008, International Journal of Computer Vision.

[13]  Jake K. Aggarwal,et al.  A hierarchical Bayesian network for event recognition of human actions and interactions , 2004, Multimedia Systems.

[14]  Antonello Cutolo,et al.  An optical fiber intrusion detection system for railway security , 2017 .

[15]  Álvaro Hernández,et al.  Efficient Multisensory Barrier for Obstacle Detection on Railways , 2010, IEEE Transactions on Intelligent Transportation Systems.

[16]  Deva Ramanan,et al.  Histograms of Sparse Codes for Object Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Luc Van Gool,et al.  Temporal Segment Networks: Towards Good Practices for Deep Action Recognition , 2016, ECCV.

[18]  Alexandrina Rogozan,et al.  Intensity self similarity features for pedestrian detection in Far-Infrared images , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[19]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[20]  Yu-Hsiang Fu,et al.  Automatic Single-Image-Based Rain Streaks Removal via Image Decomposition , 2012, IEEE Transactions on Image Processing.

[21]  Tong Leia.  Railway Obstacle Detection using Onboard Forward-Viewing Camera , 2012 .

[22]  Jun Wang,et al.  Fusing Multi-Stream Deep Networks for Video Classification , 2015, ArXiv.

[23]  Zhang Junfen.  Improved Algorithm for Image Dehazing Using Dark Channel Prior , 2013 .

[24]  Kun-Hong Lee,et al.  Histograms of local intensity differences for pedestrian classification in far-infrared images , 2013 .

[25]  Jenq-Neng Hwang,et al.  Object-based analysis and interpretation of human motion in sports video sequences by dynamic bayesian networks , 2003, Comput. Vis. Image Underst..

[26]  Andrea Vedaldi,et al.  Dynamic Image Networks for Action Recognition , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Masaaki Yoneda,et al.  Real-time snowfall noise elimination , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[28]  Hai Liu,et al.  Detecting and extracting natural snow from videos , 2010, Inf. Process. Lett..

[29]  Luis Fonseca,et al.  Video based system for railroad collision warning , 2012, 2012 IEEE International Carnahan Conference on Security Technology (ICCST).

[30]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Jian Sun,et al.  Single image haze removal using dark channel prior , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.