自适应铁路场景前景目标检测

交通运输系统工程与信息 ›› 2020, Vol. 20 ›› Issue (2): 83-90.

• 智能交通系统与信息技术 • 上一篇下一篇

自适应铁路场景前景目标检测

李兴鑫，朱力强^*，余祖俊

北京交通大学机械与电子控制工程学院，北京 100044

收稿日期:2019-11-19 修回日期:2019-12-19 出版日期:2020-04-25 发布日期:2020-04-30
作者简介:李兴鑫(1993-)，女，甘肃天水人，博士生.
基金资助:
国家重点研发计划/National Key Research and Development Program of China (2016YFB1200401).

Adaptive Foreground Object Detection in Railway Scene

LI Xing-xin, ZHU Li-qiang, YU Zu-jun

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China

Received:2019-11-19 Revised:2019-12-19 Online:2020-04-25 Published:2020-04-30

摘要/Abstract

摘要：

现代铁路系统中，智能视频分析技术已被广泛应用于异物入侵监测，前景目标检测是入侵判断的必要过程. 背景差分常用于检测前景目标，但铁路场景复杂，存在动态变化的背景区域和未知类型的目标，现有基于阈值分割或深度学习的背景差分算法都不能满足需求，故提出一种基于阈值自适应调节的前景目标检测算法. 利用像素值在时间上的动态信息，分割结果的反馈信息和由超像素提供的空间信息确定阈值调节因子，动态调节阈值以适应环境变化；提出一种灵活可靠的背景模型初始化方法，消除鬼影问题，实现一帧到多帧初始化的灵活切换. 实验结果表明，所提算法在铁路场景上取得了较好的准确率和误分类率，且平衡了精度和速度.

关键词: 信息技术, 自适应阈值, 背景差分, 铁路异物入侵, 前景检测

Abstract:

In modern railway systems, intelligent video analysis has been widely applied in foreign body intrusion monitoring, and foreground object detection is an essential step for intrusion detection. Background subtraction is commonly used to detect foreground objects. However, existing threshold- segmentation- based methods and deep-learning-based methods cannot meet the requirements in a complex railway scene, which contains dynamic background and unknown objects. In this paper, we proposed an adaptive- threshold- based foreground detection algorithm, which utilizes the temporal dynamic of pixel intensity, feedback information of detection result and spatial information of super- pixel to determine a factor, and then automatically adjusts the threshold by the factor to follow scene change. In addition, we also proposed a flexible and reliable background model initialization method that eliminates the ghost problem and flexibly switches from one-frame initialization to multiple- frame initialization. Experimental results show that the proposed algorithm achieves better accuracy and wrong classification rate in railway scenes, and also gets a better trade-off between accuracy and speed.

Key words: information technology, adaptive threshold, background subtraction, foreign body intrusion of railway, foreground detection

中图分类号:

U298.1

李兴鑫，朱力强，余祖俊. 自适应铁路场景前景目标检测[J]. 交通运输系统工程与信息, 2020, 20(2): 83-90.

LI Xing-xin, ZHU Li-qiang, YU Zu-jun. Adaptive Foreground Object Detection in Railway Scene[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(2): 83-90.

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参考文献

[1] 王洋, 朱力强, 余祖俊, 等. 高速铁路场景的分割与识别算法[J]. 光学学报, 2019, 39(6): 119-126. [WANG Y, ZHU L Q, YU Z J, et al. Segmentation and recognition algorithm for high- speed railway scene[J]. Acts Optica Sinica, 2019, 39(6): 119-126.]

[2] 王忠立, 蔡伯根. 一种基于几何约束的轨道提取方法研究[J]. 交通运输系统工程与信息, 2017, 17(6): 56- 62, 84. [WANG Z L, CAI B G. Geometry constraintsbased method for visual rail track extraction[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(6): 56-62, 84.]

[3] 王洋, 余祖俊, 朱力强, 等. 基于CNN的高速铁路侵限异物特征快速提取算法[J]. 仪器仪表学报, 2017, 38 (5): 1267-1275. [WANG Y, YU Z J, ZHU L Q, et al. Fast feature extraction algorithm for high-speed railway clearance intruding objects based on CNN[J]. Chinese Journal of Scientific Instrument, 2017, 38(5): 1267- 1275.]

[4] 牛宏侠, 张肇鑫, 宁正, 等. 铁路轨道异物完整性检测与跟踪算法研究[J]. 交通运输系统工程与信息, 2019, 19(1): 45-54. [NIU H X, ZHANG Z X, NING Z, et al. Detection and tracking algorithm of foreign integrity in railway tracks[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(1): 45-54.]

[5] ELGAMMAL A, Harwood D, Davis L. Non-parametric model for background subtraction[C]. European Conference on Computer Vision (ECCV), Ireland, Springer Berlin Heidelberg, 2000: 751-767.

[6] BARNICH O, VAN DROOGENBROECK M. ViBe: A universal background subtraction algorithm for video sequences[J]. IEEE Transactions on Image Processing, 2011, 20(6): 1709-1724.

[7] HOFMANN M, TIEFENBACHER P, RIGOLL G. Background segmentation with feedback: The pixelbased adaptive segmenter[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), USA, IEEE, 2012: 38-43.

[8] ST-CHARLES P L, BILODEAU G A, BERGEVIN R. SubSENSE: A universal change detection method with local adaptive sensitivity[J]. IEEE Transactions on Image Processing, 2014, 24(1): 359-373.

[9] JIANG S, LU X. WeSamBE: A weight- sample- based method for background subtraction[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2018, 28(9): 2105-2115.

[10] CHEN T Y, BIGLARI- ABHARI M, WANG I K. SuperBE: Computationally light background estimation with superpixels[J]. Journal of Real-Time Image Processing, 2019, 16(6): 2319-2335.

[11] BOUWMANS T, JAVED S, SULTANA M, et al. Deep neural network concepts for background subtraction: A systematic review and comparative evaluation[J]. Neural Networks, 2019(117): 8-66.

[12] BABAEE M, DINH D T, RIGOLL G. A deep convolutional neural network for video sequence background subtraction[J]. Pattern Recognition, 2018 (76): 635-649.

[13] LIN C, YAN B, TAN W. Foreground detection in surveillance video with fully convolutional semantic network[C]. IEEE International Conference on Image Processing (ICIP), Greece, IEEE, 2018: 4118-4122.

[14] LONG A L, KELES H Y. Learning multi-scale features for foreground segmentation[J]. Arxiv, 2018, DOI: 10.1007/s10044-019-00845-9.

[15] WANG Y, YU Z, ZHU L. Foreground detection with deeply learned multi-scale spatial-temporal features[J]. Sensors, 2018, 18(12): 4269.

[16] ACHANTA R, SHAJI A, SMITH K, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2282.

自适应铁路场景前景目标检测

Adaptive Foreground Object Detection in Railway Scene

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