计算机科学 ›› 2018, Vol. 45 ›› Issue (4): 291-295.doi: 10.11896/j.issn.1002-137X.2018.04.049

• 图形图像与模式识别 • 上一篇    下一篇

基于视频的矿井中人体运动区域检测

李珊,饶文碧   

  1. 武汉理工大学计算机科学与技术学院 武汉430070,武汉理工大学计算机科学与技术学院 武汉430070
  • 出版日期:2018-04-15 发布日期:2018-05-11
  • 基金资助:
    本文受国家自然科学基金项目(61601337),湖北省重大科技创新计划项目(2015BCE068)资助

Video-based Detection of Human Motion Area in Mine

LI Shan and RAO Wen-bi   

  • Online:2018-04-15 Published:2018-05-11

摘要: 将人体运动区域检测技术应用到矿井视频中可以检测矿井下矿工的运动情况,进一步可以智能检测矿工的异常行为,根据反馈的检测结果实现实时报警和联动控制,减少矿井事故的发生。针对矿井场景下的人体运动区域检测,提出了一种实现人体运动区域提取的融合方法TD-HF(Time Difference and Haar Feature),该方法融合了时间差分法和基于Haar特征的人体检测算法。实验表明,所提方法在检测率和误识率方面均比单纯的基于AdaBoost算法的分类器更胜一筹,并且在检测时间上满足实时性要求,适用于矿井视频这种特殊场景下的人体运动区域检测。

关键词: 人体运动区域,时间差分法,TD-HF,AdaBoost

Abstract: The human motion area detection technology applied to the mine video can detect motion of miners and intelligently detect abnormal behavior of miners through further analysis.According to the results of feedback detection to achieve real-time alarm and linkage control,it obviously reduces the occurrence of mine accidents.This paper proposed a hybrid method TD-HF(Time Difference and Haar Feature) for extracting human motion area,which integrates the time difference method and the human detection algorithm based on Haar feature especially under the condition of mine.The experiment shows that this method is better than the simple classifier based on AdaBoost algorithm in the detection rate and false recognition rate,at the same time,it can satisfy the real-time requirements in detection time.It’s applicable to the detection of human motion area under the special condition of mine video.

Key words: Human motion region,Time difference method,TD-HF,AdaBoost

[1] WU Y, XU L H,LI D W,et al.An Improved Moving Object Tracking Algorithm Based on Optical Flow Method[J].Mechatronics,2011,7(12):18-25,74.(in Chinese) 吴阳,徐立鸿,李大威,等.一种改进的基于光流法的运动目标跟踪算法[J].机电一体化,2011,17(12):18-25,74.
[2] LI W S,LI H F.A background subtraction method based onadaptive hybrid model[J].Computer Engineering & Science,2016,8(10):2091-2100.(in Chinese) 李伟生,李辉飞.一种基于自适应混合模型的背景减除法[J].计算机工程与科学,2016,38(10):2091-2100.
[3] XUE L X,LUO Y L,WANG Z C.Detection algorithm of adaptive moving objects based on frame difference method[J].Application Research of Computers,2011,8(4):1551-1552.(in Chinese) 薛丽霞,罗艳丽,王佐成.基于帧间差分的自适应运动目标检测方法[J].计算机应用研究,2011,28(4):1551-1552.
[4] AKHAND M A H,AKASH A A,MOLLAH A S.Improvement of Haar Feature Based Face Detection in OpenCV Incorporating Human Skin Color Characteristic[J].International Journal of Computer Applications & Information Technology,2016,1(1):8.
[5] SAEED A,AL-HAMADI A,GHONEIM A.Head Pose Estimation on Top of Haar-Like Face Detection:A Study Using the Kinect Sensor[J].SENSORS,2015,5(9):20945-20966.
[6] LIU C,CHANG F,LIU C.Cascaded split-level colour Haar-like features for object detection[J].Electronics Letters,2015,1(25):2106-2107.
[7] SAVAS B K,ILKIN S,BECERIKLI Y.The realization of face detection and fullness detection in medium by using Haar Cascade Classifiers[C]∥Signal Processing and Communication Application Conference.2016:2217-2220.
[8] WEI L,LIU M.Multi-pose Face Detection Research Based on Adaboost[C]∥Eighth International Conference on Measuring Technology and Mechatronics Automation.IEEE Computer Society,2016:409-412.
[9] SUN B,CHEN S,WANG J,et al.A robust multi-class Ada-Boost algorithm for mislabeled noisy data[J].Knowledge-Based Systems,2016,2(5):87-102.
[10] ZHANG L,YIN J,LIN J,et al.Detection of coronal mass ejections using AdaBoost on grayscale statistic features[J].New A stronomy,2016,8:49-57.
[11] JIA X,ZHU Q,ZHANG P,et al.Face Feature Points Detection Based on Adaboost and AAM[C]∥4th International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem(GRMSE 2016).2016:142-149.
[12] ZHOU H,et al.Marine object detection using background mo-delling and blob analysis[C]∥IEEE International Conference on Systems Man and Cybernetics Conference Proceedings.2015:430-435.
[13] ATIBI M,ATOUF I,BOUSSAA M,et al.Real-time detection of vehicles using the haar-like features and artificial neuron networks[C]∥International Conference on Advanced Wireless Information and Communication Technologies.2015:24-31.
[14] SHARIFARA A,MOHD RAHIM M S,ANISI Y.A general review of human face detection including a study of neural networks and Haar feature-based cascade classifier in face detection[C]∥International Symposium on Biometrics and Security Technologies.IEEE,2015:73-78.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 编辑部. 新网站开通,欢迎大家订阅![J]. 计算机科学, 2018, 1(1): 1 .
[2] 雷丽晖,王静. 可能性测度下的LTL模型检测并行化研究[J]. 计算机科学, 2018, 45(4): 71 -75 .
[3] 孙启,金燕,何琨,徐凌轩. 用于求解混合车辆路径问题的混合进化算法[J]. 计算机科学, 2018, 45(4): 76 -82 .
[4] 张佳男,肖鸣宇. 带权混合支配问题的近似算法研究[J]. 计算机科学, 2018, 45(4): 83 -88 .
[5] 伍建辉,黄中祥,李武,吴健辉,彭鑫,张生. 城市道路建设时序决策的鲁棒优化[J]. 计算机科学, 2018, 45(4): 89 -93 .
[6] 史雯隽,武继刚,罗裕春. 针对移动云计算任务迁移的快速高效调度算法[J]. 计算机科学, 2018, 45(4): 94 -99 .
[7] 周燕萍,业巧林. 基于L1-范数距离的最小二乘对支持向量机[J]. 计算机科学, 2018, 45(4): 100 -105 .
[8] 刘博艺,唐湘滟,程杰仁. 基于多生长时期模板匹配的玉米螟识别方法[J]. 计算机科学, 2018, 45(4): 106 -111 .
[9] 耿海军,施新刚,王之梁,尹霞,尹少平. 基于有向无环图的互联网域内节能路由算法[J]. 计算机科学, 2018, 45(4): 112 -116 .
[10] 崔琼,李建华,王宏,南明莉. 基于节点修复的网络化指挥信息系统弹性分析模型[J]. 计算机科学, 2018, 45(4): 117 -121 .