視訊辨識技術應用於智慧型監控系統之研究

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doi:10.6342/NTU201603459

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视讯辨识技术应用於智慧型监控系统之研究

Vision Sensing Techniques for Intelligent Surveillance System

陈宣辑 , 博士　　导师：洪一平　　

英文 DOI： 10.6342/NTU201603459

电脑视觉；智慧监控；物件侦测；行人比对；人脸对位；人脸辨识；摄影机异常侦测；遗留物侦测；智慧型监控介面； Computer Vision ； Intelligent Surveillance System ； Object Detection ； Person Re-Identification ； Face Alignment ； Face Recognition ； Camera Tampering Detection ； Abandoned Luggage Detection ； Intelligent Visualization

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摘要 │ 参考文献 (144) │ 国际计量

摘要〈TOP〉

随着智慧型监控系统的发展，影像分析与辨识技术已经成为智慧型监控系统内最重要的核心技术。本研究以建构全方位的智慧型监控系统为目标，提出多项前端影像辨识技术，包含：监控摄影机之干扰侦测、监控摄影机之行人与物件侦测、行人脸部定位技术、行人遗留物侦测、行人比对技术与智慧型监控人机介面。智慧型影像监控系统利用摄影机为主要讯号输入，透过电脑视觉影像辨识技术达到自动监控的目标。因此如何保护摄影机为首要之任务，我们提出即时摄影机干扰侦测技术，此演算法能透过摄影机输入影像，判定摄影机是否遭受人为蓄意遮蔽、转向、失焦、断线等破坏，此方法侦测影像中的关键点，并侦测其变化，达成低计算成本之优点并在多个实际测试影片中获得相当稳定结果：低误判率与高准确度。确保摄影机安全後，我们利用固定式摄影机场景的特性，也就是行人在摄影机不同位置拥有的一致性特徵，透过自动取样此摄影机下的行人与物件，自动学习其显着特徵并训练出多个特定区域行人精炼侦测器，每个侦测器负责摄影机下之局部范围。与目前仅使用单一侦测器方法实验比较後发现，本方法能大幅提升的物件侦测之准确度。

此外，我们也提出行人比对技术，当给予一个嫌疑者於摄影机中的照片後，此技术整合局部与整体外观特徵，达成高准确度之行人比对技术，能在摄影机网路所有的行人资料中快速找到相符者。除了行人外观为一项重要特徵外，脸部资讯也是不可缺少的影像线索，我能提出脸部特徵对位侦测技术，利用具深度资训的人脸训练影像，离线建立3D人脸模型，并於侦测时套用於於二维影像上。与现今仅使用平面二维资讯之方法比较後发现，多了三维模型的资讯能使对位结果更为准确；此外，由於我们有3D人脸模型，因此在脸部对位後，我们能直接获的脸部旋转资讯，提供智慧型监控系统更多有关行人的资讯，例如：在智慧型人流分析系统中，我们可利用脸部角度与行经路径估测行人所关注的区域与商品。

除了行人侦测与辨识外，我们也试着进行摄影机下的人为分析，我们以行人遗留物侦测为范例，在影像上的每个像素建立前景/背景状态有限状态机，分析该像素的状态转换与变化过程，决定是否在画面中出现静止不动的前景物。为了完整分析遗留物的事件，我们追朔过去一段时间内的移动物体轨迹，分析并验证物主是否确实远离了遗留物，以减少误报情形，此方法在两个公开测试资料库(PETS2006、AVSS2007、NTU) 的侦测数据上均胜过相关研究。最後基於以上核心技术，我们再提出两项先进的人机显示方法，方便监控者快速了解、观看并搜索多摄影机网路内所有行人与事件。

并列摘要〈TOP〉

With the development of intelligent surveillance systems, video analysis, and recognition technology have become the most important core techniques in this field. In order to construct a surveillance system with higher intelligence, this research proposes a number of advanced video recognition technologies, including the camera interference/tampering detection, pedestrian detection, abandoned luggage detection, pedestrian re-identification and intelligent interface for visualization. Video surveillance uses cameras as the primary input sensor to achieve automatic monitoring. Therefore, how to protect the camera has become the top priority. We propose real-time camera sabotage/tampering detection technology which quickly detects whether or not cameras are hindered by deliberate shelter, disorientation, out of focus, disconnection and other damage via the video analysis. We initially locate the key points whose appearances are relatively stable. Monitoring the changes of these key points and scene structure can detect the tampering events precisely and efficiently. Our method requires lower computational cost and obtains higher stability and accuracy rate in comparison to the existing methods.  After protecting cameras, we propose a scene-specific pedestrian detection and object classification. Our approach is location-based, which cab discover scene-dependent discriminative features to identifying foreground objects of different categories (e.g., pedestrians, bicycles, and vehicles). We incorporate a similarity grouping procedure capable of gathering more consistent training examples from a considerably larger neighbor area and train the　specific pedestrian detectors for each grouped local area. Our approach gets significant improvement in detection and classification comparing the traditional generic object detector and classifier.

Also, we propose an ensemble of invariant features (EIF), which can properly handle the color variations and human poses/viewpoints for matching pedestrian images observed in different cameras. Our proposed method belongs the direct method, which requires no domain learning. The novel features combined both the holistic and region-based features. The holistic features are extracted by using a publicly available pre-trained deep convolutional neural network (DCNN) used in generic object classification. In contrast, the region-based features are extracted based on our proposed two-way Gaussian Mixture Model fitting (2WGMMF), which overcomes the self-occlusion and poses variations.

In addition to the appearance feature, the face information is undoubtedly the indispensable vital in video surveillance. We propose a 3D face alignment algorithm in the 2D image based on Active Shape Model. We off-line train a 3D shape model with different view-based local texture models from a 3D database, and then on-line fit a face in a 2D image by these models. This method mainly leverages additional depth information on the traditional 2D image alignment problem and gets a promising improvement compared to the existing model-based and regression-based approaches. Since the human poses, and their gaze directions are especially valuable information to the surveillance system, the head poses can be directly estimated by the alignment result of the proposed 3D model subsequently.

Based on the robust pedestrian detection and re-identification algorithm, we also focus the problem of event detection in surveillance cameras. We take the abandoned luggage detection as an example since it is one of the most critical and challenge problems in video surveillance. We propose the complementary background model which combines short- and long-term background models to classify each pixel as 2-bit code where each bit represents a foreground or background. Subsequently, we introduce a finite-state machine framework to identify static foreground regions based on the temporal transition of code patterns and to determine whether the selected area contain abandoned objects by analyzing the back-traced trajectories of luggage owners. The experimental results obtained based on video images from 2006 Performance Evaluation of Tracking and Surveillance (PETS2006), 2007 Advanced Video, Signal-based Surveillance (AVSS2007) databases and NTU data set collected by ourselves. We show that the proposed approach is useful for detecting abandoned luggage and that it outperforms previous methods.

Finally, based on the above core technologies, we also propose two advanced visualization interface, which facilitates people to observe quickly and search incidents of pedestrians within a camera network.

参考文献 ( 144 ) 〈TOP〉

[2] Yu Su Bingpeng Ma and Frederic Jurie. Covariance descriptor based on bio-inspired features for person re-identification and face verification. Image and Vision Computing, 32(6):379–390, 2014.
连结：
[5] Douglas Gray and Hai Tao. Viewpoint invariant pedestrian recognition with an ensemble of localized features. In Proc. of the European Conference on Computer Vision (ECCV), pages 262–275, Marseille, France, 2008.
连结：
[6] Davide Baltieri, Roberto Vezzani, and Rita Cucchiara. Mapping appearance descriptors on 3d body models for people re-identification. International Journal of Computer Vision (IJCV), 111(3):345–364, 2015.
连结：
[7] Wei Li, Rui Zhao, Tong Xiao, and Xiaogang Wang. Deepreid: Deep filter pairing neural network for person re-identification. In Proc.of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 152–159, Columbus, OH, 2014.
连结：
[9] Fatih Porikli, Yuri Ivanov, and Tetsuji Haga. Robust abandoned object detection using dual foregrounds. EURASIP Journal on Advances in Signal Processing, 2008:30, 2008.
连结：

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DOI可强化引用精确性、增强学术圈连结，并给予使用者跨平台的良好使用经验，目前在全世界已有超过五千万个对象申请DOI。如想对DOI的使用与概念有进一步了解，请参考（）。