视频监控场景中的遗留物检测研究与实现
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摘要
随着社会经济的发展,人们对安全防范的需求也越来越大。例如,机场、地铁、候车厅等人多密集的重要场所,容易被恐怖分子等利用,通常具有很大的安全隐患,因此需要进行实时监测。而传统的监控系统基本只能用于事后证据呈现,却不能够实时检测异常事件发生。所以人们期待一种智能化的技术用于安全防范,为此出现了智能视频监控系统。
本文主要研究监控场景中包裹遗留事件的检测。关于遗留事件的判别,目前主要有基于目标检测的方法和基于目标跟踪的方法,但现有算法多数并不鲁棒。在已有的基于目标检测的方法中,由于初始背景在慢背景更新模型中具有较大的权值,若初始背景存在运动目标,该目标区域不能及时更新背景,则该区域易被判别为遗留目标区域；当监控场景中有人体停留时间过长,并且保持不动,也会误将人体判别为遗留物体。在背景更新阶段,前景物体有可能很快融入到背景中,如果更新速率选择不好,有可能会出现“鬼影”现象等。基于目标跟踪的方法主要是对运动目标的位置进行预测估计,这种方法容易受光线等因素的影响。人体等非刚性物体在跟踪时很难用模板的形式完成检测和跟踪,从而导致预测不准确,以致丢失检测目标。
本文对相关算法进行了综述,分析了遗留物检测原理和重难点,在已有成果的基础上做出了一些改进和创新。本文的工作内容如下：
第一,针对现有的遗留物检测算法,分析了遗留物检测原理、遗留物检测的重点和难点以及实现步骤。
第二,本文主要采用基于目标检测的方法,首先通过双背景模型检测暂时静止目标,双背景模型分别采用慢背景更新的滑动平均背景模型和快背景更新的混合高斯背景模型,然后利用Blob跟踪的方法对每一个暂时静止目标进行跟踪,分析目标边缘和中心—外围直方图信息,判别目标是遗留目标还是遗失目标。
第三,算法中加入了基于HOG的人体检测,对目标感兴趣区域进行人体检测,从而消除因行人驻留产生的误判。
最后,编程实现了遗留物检测系统,并对下载的标准视频库和采集的场景模拟视频进行测试,都取得了比较好的效果。
With the development of society and economy, people's security needs is increasing. Terrorists usually commit attacks in followed places, Such as airport, subway, and waiting hall and so on. So a real time monitored system is needed. But traditional monitoring system can only be used for the basic evidence and not capable of real-time detecting abnormal events, so people look forward to a kind of intelligent technology for security. So the intelligent video surveillance system is emerging.
The main study in this paper is detecting abandoned object in surveillance video. So far the abandoned event detection is based on target detection and target tracking, but they are not robust sometimes. For example, the method which based on target detection can do false alarm, due to the initial background scene has the bigger weight in slow update rate background model and if has some move target, the area of the move target can't update in time, so the area easily be judged as abandoned object area. And when somebody enter in scene and keep still during a period time, the algorithm will judge the person as abandoned object. In background update stage, foreground object sometimes will quickly melt into the background; if the background learning rate is not good,"ghost" may be happening. Abandoned object detection which based on tracking method, mainly focus on the moving target position prediction and identification, the method easily affected by light and other factors, human body as non-rigid objects in tracking is difficult to use a template to detect and track, often leads to inaccurate prediction, so that target will lose sometimes.
In this paper, first of all, I do amount of research and reviewed on the basis of related algorithms, and then analysis the principle and difficulty, made some improvements and creation based on the existing achievement. The works and creation of this paper are as follows:
Firstly, review the existing detection algorithm, do analysis on detecting principle, key points, difficulty and the step of implementation.
Secondly, the main idea of this paper is based on detection method. At the beginning detect temporarily stationary target using the dual-background model, they are slow update moving average background model and fast update Mixture Gauss background model. And then, using Blob tracks every target which detected at beginning. Do analysis on target edge and center-peripheral region histogram, discriminate the target whether abandoned or not.
Thirdly, human detection based on HOG (Histogram of Oriented Gradient) joint in proposed algorithm can detect human body and eliminate misjudgment caused by human keep still in scene.
Finally, implement the detection system. Through testing the video which are from the online standard video lib and captured by ourselves, the system and the algorithm have achieved good results.
本文主要研究监控场景中包裹遗留事件的检测。关于遗留事件的判别,目前主要有基于目标检测的方法和基于目标跟踪的方法,但现有算法多数并不鲁棒。在已有的基于目标检测的方法中,由于初始背景在慢背景更新模型中具有较大的权值,若初始背景存在运动目标,该目标区域不能及时更新背景,则该区域易被判别为遗留目标区域；当监控场景中有人体停留时间过长,并且保持不动,也会误将人体判别为遗留物体。在背景更新阶段,前景物体有可能很快融入到背景中,如果更新速率选择不好,有可能会出现“鬼影”现象等。基于目标跟踪的方法主要是对运动目标的位置进行预测估计,这种方法容易受光线等因素的影响。人体等非刚性物体在跟踪时很难用模板的形式完成检测和跟踪,从而导致预测不准确,以致丢失检测目标。
本文对相关算法进行了综述,分析了遗留物检测原理和重难点,在已有成果的基础上做出了一些改进和创新。本文的工作内容如下：
第一,针对现有的遗留物检测算法,分析了遗留物检测原理、遗留物检测的重点和难点以及实现步骤。
第二,本文主要采用基于目标检测的方法,首先通过双背景模型检测暂时静止目标,双背景模型分别采用慢背景更新的滑动平均背景模型和快背景更新的混合高斯背景模型,然后利用Blob跟踪的方法对每一个暂时静止目标进行跟踪,分析目标边缘和中心—外围直方图信息,判别目标是遗留目标还是遗失目标。
第三,算法中加入了基于HOG的人体检测,对目标感兴趣区域进行人体检测,从而消除因行人驻留产生的误判。
最后,编程实现了遗留物检测系统,并对下载的标准视频库和采集的场景模拟视频进行测试,都取得了比较好的效果。
With the development of society and economy, people's security needs is increasing. Terrorists usually commit attacks in followed places, Such as airport, subway, and waiting hall and so on. So a real time monitored system is needed. But traditional monitoring system can only be used for the basic evidence and not capable of real-time detecting abnormal events, so people look forward to a kind of intelligent technology for security. So the intelligent video surveillance system is emerging.
The main study in this paper is detecting abandoned object in surveillance video. So far the abandoned event detection is based on target detection and target tracking, but they are not robust sometimes. For example, the method which based on target detection can do false alarm, due to the initial background scene has the bigger weight in slow update rate background model and if has some move target, the area of the move target can't update in time, so the area easily be judged as abandoned object area. And when somebody enter in scene and keep still during a period time, the algorithm will judge the person as abandoned object. In background update stage, foreground object sometimes will quickly melt into the background; if the background learning rate is not good,"ghost" may be happening. Abandoned object detection which based on tracking method, mainly focus on the moving target position prediction and identification, the method easily affected by light and other factors, human body as non-rigid objects in tracking is difficult to use a template to detect and track, often leads to inaccurate prediction, so that target will lose sometimes.
In this paper, first of all, I do amount of research and reviewed on the basis of related algorithms, and then analysis the principle and difficulty, made some improvements and creation based on the existing achievement. The works and creation of this paper are as follows:
Firstly, review the existing detection algorithm, do analysis on detecting principle, key points, difficulty and the step of implementation.
Secondly, the main idea of this paper is based on detection method. At the beginning detect temporarily stationary target using the dual-background model, they are slow update moving average background model and fast update Mixture Gauss background model. And then, using Blob tracks every target which detected at beginning. Do analysis on target edge and center-peripheral region histogram, discriminate the target whether abandoned or not.
Thirdly, human detection based on HOG (Histogram of Oriented Gradient) joint in proposed algorithm can detect human body and eliminate misjudgment caused by human keep still in scene.
Finally, implement the detection system. Through testing the video which are from the online standard video lib and captured by ourselves, the system and the algorithm have achieved good results.
引文
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[8]R.Feris, Y.Tian and A.Hampapur. Capturing People in Surveillance Video[C]. IEEE International Workshop on Visual Surveillance,2007.
[9]Beynon M, Hook D, Seibert M. Detecting Abandoned Packages in a Multi-Camera Video Surveillance System[C]. Proc. of the IEEE Conf. on Advanced Video and Signal Based Surveillance,2003:221-228.
[10]Chuan-Yu Cho, Wei-Hao Tung and Jia-Shung Wang. A CROWD-FILTER FOR DETECTION OF ABANDONED OBJECTS IN CROWDED AREA[C].3rd International Conference on Sensing Technology,2008:581-584
[11]Dorin Comaniciu, Visvanathan Ramesh. Kernel-Based Object Tracking[J]. IEEE trans.,2003.25(5):564-577
[12]Chi-Hung Chuang and Jun-Wei Hsieh. Carried Object Detection Using Ratio Histogram and its Application to Suspicious Event Analysis. IEEE trans, 2009,19(6):911-916.
[13]Medha Bhargava and Chia-Chih Chen. Detection of object abandonment using temporal logic. Machine Vision and Applications,2009,20:271-281.
[14]Jianting Wen, Haifeng Gong et. GENERATIVE MODEL FOR ABANDONED OBJECT DETECTION[C]. ICIP 2009:853-856
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[19]Milan Sonka, Vaclav Hlavac, Roger Boyle. IMAGE PROCESSING, ANALYSIS, AND MACHINE VISION.艾海舟,苏延超等译.图像处理、分析与机器视觉.清华大学出版社2011年11月第二次印刷.
[20]李忠武,高广珠等.图像序列目标检测中阴影的消除[J].计算机应用与研究,2004,21(5)：205-206.
[21]王华伟,李翠华,施华等.基于HSV空间一阶梯度阴影的剪除[J].计算机工程与应用,2005,41(8)：43-44.
[22]Horn, Berthold K.P., Schunck Brian G. Determining Optical Flow[J]. Artificial Intelligence,1981,17(13):185-203.
[23]Anderson C,Burt P,Wal G.Change detection and tracking using pyramid transformation techniques. In Proc. of SPIE-Intelligent Robots and Computer Vision.1985,579:72-78
[24]Lipton A, Fujiyoshi H, Patil R. Moving target classification and tracking from real-time video[C].In:Proc of WACV'98,8-14,1998.
[25]艾海舟,吕风军.面向视觉监视的变化检测与分割.计算机工程与应用,2001,37(5)：75-77
[26]Stauffer C, Grimson W.E.L. Adaptive Background Mixture Models for Real Time Tracking. In Proc. Computer Vision and Pattern Recognition 1999(CVPR'99), 246-252 June 1999.
[27]P. Spagnolo, A. Caroppo, M. Leo, T.o Martiriggiano, and T. D'Orazio. An abandoned/removed objects detection algorithm and its evaluation on pets datasets. In Proceedings of the IEEE International Conference on Video and Signal Based Surveillance,2006, pp.17-22.
[28]Claudio Sacchi, Carlo S. Regazzoni. A Distributed Surveillance System for Detection of Abandoned Objects in Unmanned Railway Environments. IEEE Trans. ON VEHICULAR TECHNOLOGY.2000,49(5):2013-2026
[29]S. Denman, S. Sridharan, and V. Chandran. Abandoned object detection using multi-layer motion detection. In Proceeding of International Conference on Signal Processing and Communication Systems,2007.
[30]F. Porikli. Detection of Temporarily Static Regions by Processing Video at Different Frame Rates[C].2007 IEEE conference on advanced video and signal based surveillance.2007.236-241.
[31]F. Porikli, Y. Ivanov, and T. Haga, "Robust Abandoned Object Detection Using Dual Foregrounds", Eurasip Journal on Advances in Signal Processing, vol.2008, 2008.
[32]J. Shi, C. Tomasi. Good Feature to track. Peoceedings of IEEE Conference on Computer Vision and pattern Recognition,1994,593-600
[33]Cheng, Y, "Mean Shift, Mode Seeking, and Cluster-ing," IEEE Trans. Pattern Analysis and Machine Intel-ligence, Vol 17(8), August 1995, pp.790-799
[34]R. T. Collins. Mean-shift blob tracking through scale space. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition,2003.
[35]M. Isard, A. Blake. CONDENSATION-Conditional Density Propagation for Visual Tracking. International Journal of Computer Vision,1998,29(1):5-28
[36]A. Yilmaz, Xin Li, M. Shah. Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras. IEEE Trans. Pattern Analysis and Machine Intelligence,2004,26(11):1531-1536
[37]Gregory D. Hager, Peter N. Belhumeur. Efficient Region Tracking With Parametric Models of Geometry and Illumination. IEEE Trans. On Pattern Analysis and Machine Intelligence.1998,20(10):1025-1039
[38]Yu Huang, Thomas S.Huang, Heinrich Niemann. A Region-Based Method for Model Free Object Tracking. International Conference on Pattern Recognition, 2002:592-595
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[2]王晨.智能视频监控系统设计[D].南京：南京理工大学,2007
[3]Green Mary W. The Application and Effective Use of Security Technologies in U.S. Schools:A Guide for Schools and Law Enforcement Agencies[R]. NCJ 178625, Washington DC, USA:National Institute of Justice, U.S. Department of Justice,1999.
[4]王素玉,沈兰荪.智能视觉监控技术研究进展[J].中国图像图形学报,2007,12(9)：1505-1514
[5]Haritaoglu I, Harwood D and Davis L. W4:Real-Time Surveillance of People and Their Activities [J]. IEEE trans.,200022(8):809-830
[6]T.collins, AJ.Lipton, T.Kanade, H.Fujiyoshi, etc. A System for Video Surveillance and Monitoring[R], VSAM Final Report, Technical report CMU-RI-TR,1-12.
[7]Y. Tian, R. Feris and A. Hampapur. Real-Time Detection of Abandoned and Removed Object in Complex Environments[C]. The Eighth International Workshop Visual Surveillance.2008.95-102.
[8]R.Feris, Y.Tian and A.Hampapur. Capturing People in Surveillance Video[C]. IEEE International Workshop on Visual Surveillance,2007.
[9]Beynon M, Hook D, Seibert M. Detecting Abandoned Packages in a Multi-Camera Video Surveillance System[C]. Proc. of the IEEE Conf. on Advanced Video and Signal Based Surveillance,2003:221-228.
[10]Chuan-Yu Cho, Wei-Hao Tung and Jia-Shung Wang. A CROWD-FILTER FOR DETECTION OF ABANDONED OBJECTS IN CROWDED AREA[C].3rd International Conference on Sensing Technology,2008:581-584
[11]Dorin Comaniciu, Visvanathan Ramesh. Kernel-Based Object Tracking[J]. IEEE trans.,2003.25(5):564-577
[12]Chi-Hung Chuang and Jun-Wei Hsieh. Carried Object Detection Using Ratio Histogram and its Application to Suspicious Event Analysis. IEEE trans, 2009,19(6):911-916.
[13]Medha Bhargava and Chia-Chih Chen. Detection of object abandonment using temporal logic. Machine Vision and Applications,2009,20:271-281.
[14]Jianting Wen, Haifeng Gong et. GENERATIVE MODEL FOR ABANDONED OBJECT DETECTION[C]. ICIP 2009:853-856
[15]F. Porikli. Detection of Temporarily Static Regions by Processing Video at Different Frame Rates[C].2007 IEEE conference on advanced video and signal based surveillance.2007.236-241.
[16]R. Miezianko, D. Pokrajac. Detecting and Recognizing Abandoned Objects in Crowed Environments[C]. Proc. of Computer Vision Systems.2008:241-250.
[17]汤一平,富吉勇,陈耀宇.基于全方位计算机视觉的遗留物检测系统[J].计算机测量与控制,2010,18(3)：517-523.
[18]孔英会,张新新,王蕴珠.智能视频监控系统中物体遗留检测方法的研究[J].计算机工程与科学,2010,32(6)：118-121.
[19]Milan Sonka, Vaclav Hlavac, Roger Boyle. IMAGE PROCESSING, ANALYSIS, AND MACHINE VISION.艾海舟,苏延超等译.图像处理、分析与机器视觉.清华大学出版社2011年11月第二次印刷.
[20]李忠武,高广珠等.图像序列目标检测中阴影的消除[J].计算机应用与研究,2004,21(5)：205-206.
[21]王华伟,李翠华,施华等.基于HSV空间一阶梯度阴影的剪除[J].计算机工程与应用,2005,41(8)：43-44.
[22]Horn, Berthold K.P., Schunck Brian G. Determining Optical Flow[J]. Artificial Intelligence,1981,17(13):185-203.
[23]Anderson C,Burt P,Wal G.Change detection and tracking using pyramid transformation techniques. In Proc. of SPIE-Intelligent Robots and Computer Vision.1985,579:72-78
[24]Lipton A, Fujiyoshi H, Patil R. Moving target classification and tracking from real-time video[C].In:Proc of WACV'98,8-14,1998.
[25]艾海舟,吕风军.面向视觉监视的变化检测与分割.计算机工程与应用,2001,37(5)：75-77
[26]Stauffer C, Grimson W.E.L. Adaptive Background Mixture Models for Real Time Tracking. In Proc. Computer Vision and Pattern Recognition 1999(CVPR'99), 246-252 June 1999.
[27]P. Spagnolo, A. Caroppo, M. Leo, T.o Martiriggiano, and T. D'Orazio. An abandoned/removed objects detection algorithm and its evaluation on pets datasets. In Proceedings of the IEEE International Conference on Video and Signal Based Surveillance,2006, pp.17-22.
[28]Claudio Sacchi, Carlo S. Regazzoni. A Distributed Surveillance System for Detection of Abandoned Objects in Unmanned Railway Environments. IEEE Trans. ON VEHICULAR TECHNOLOGY.2000,49(5):2013-2026
[29]S. Denman, S. Sridharan, and V. Chandran. Abandoned object detection using multi-layer motion detection. In Proceeding of International Conference on Signal Processing and Communication Systems,2007.
[30]F. Porikli. Detection of Temporarily Static Regions by Processing Video at Different Frame Rates[C].2007 IEEE conference on advanced video and signal based surveillance.2007.236-241.
[31]F. Porikli, Y. Ivanov, and T. Haga, "Robust Abandoned Object Detection Using Dual Foregrounds", Eurasip Journal on Advances in Signal Processing, vol.2008, 2008.
[32]J. Shi, C. Tomasi. Good Feature to track. Peoceedings of IEEE Conference on Computer Vision and pattern Recognition,1994,593-600
[33]Cheng, Y, "Mean Shift, Mode Seeking, and Cluster-ing," IEEE Trans. Pattern Analysis and Machine Intel-ligence, Vol 17(8), August 1995, pp.790-799
[34]R. T. Collins. Mean-shift blob tracking through scale space. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition,2003.
[35]M. Isard, A. Blake. CONDENSATION-Conditional Density Propagation for Visual Tracking. International Journal of Computer Vision,1998,29(1):5-28
[36]A. Yilmaz, Xin Li, M. Shah. Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras. IEEE Trans. Pattern Analysis and Machine Intelligence,2004,26(11):1531-1536
[37]Gregory D. Hager, Peter N. Belhumeur. Efficient Region Tracking With Parametric Models of Geometry and Illumination. IEEE Trans. On Pattern Analysis and Machine Intelligence.1998,20(10):1025-1039
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