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    • 2. 发明授权
    • Unsupervised learning of events in a video sequence
    • 无监督的视频序列中的事件学习
    • US07606425B2
    • 2009-10-20
    • US10938244
    • 2004-09-09
    • Michael E. BazakosYunqian MaVassilios Morellas
    • Michael E. BazakosYunqian MaVassilios Morellas
    • G06K9/00
    • G08B13/19606G06K9/00335G06K9/00771G08B13/19641G08B13/19673G08B13/19682G08B13/19693G08B21/0423G08B21/0476
    • Methods and systems for the unsupervised learning of events contained within a video sequence, including apparatus and interfaces for implementing such systems and methods, are disclosed. An illustrative method in accordance with an exemplary embodiment of the present invention may include the steps of providing a behavioral analysis engine, initiating a training phase mode within the behavioral analysis engine and obtaining a feature vector including one or more parameters relating to an object located within an image sequence, and then analyzing the feature vector to determine a number of possible event candidates. The behavioral analysis engine can be configured to prompt the user to confirm whether an event candidate is a new event, an existing event, or an outlier. Once trained, a testing/operational phase mode of the behavioral analysis engine can be further implemented to detect the occurrence of one or more learned events, if desired.
    • 公开了用于无监督学习视频序列中包含的事件的方法和系统,包括用于实现这种系统和方法的装置和接口。 根据本发明的示例性实施例的说明性方法可以包括以下步骤:提供行为分析引擎,在行为分析引擎内启动训练阶段模式,并获得包括与位于其内的对象有关的一个或多个参数的特征向量 图像序列,然后分析特征向量以确定可能的事件候选的数量。 行为分析引擎可以被配置为提示用户确认事件候选是否是新事件,现有事件或异常值。 一旦被训练,如果需要,可以进一步实施行为分析引擎的测试/操作阶段模式以检测一个或多个学习事件的发生。
    • 3. 发明授权
    • Method and apparatus for identifying physical features in video
    • 用于识别视频中的物理特征的方法和装置
    • US07567704B2
    • 2009-07-28
    • US11289886
    • 2005-11-30
    • Kwong Wing AuMichael E. BazakosYunqian Ma
    • Kwong Wing AuMichael E. BazakosYunqian Ma
    • G06K9/00G06K9/62G01C3/00G01C5/00G01B11/24G01B11/30H04N7/18H04N9/47H04N5/232
    • G06K9/00771G06K9/46
    • An image is processed by a sensed-feature-based classifier to generate a list of objects assigned to classes. The most prominent objects (those objects whose classification is most likely reliable) are selected for range estimation and interpolation. Based on the range estimation and interpolation, the sensed features are converted to physical features for each object. Next, that subset of objects is then run through a physical-feature-based classifier that re-classifies the objects. Next, the objects and their range estimates are re-run through the processes of range estimation and interpolation, sensed-feature-to-physical-feature conversion, and physical-feature-based classification iteratively to continuously increase the reliability of the classification as well as the range estimation. The iterations are halted when the reliability reaches a predetermined confidence threshold. In a preferred embodiment, a next subset of objects having the next highest prominence in the same image is selected and the entire iterative process is repeated. This set of iterations will include evaluation of both of the first and second subsets of objects. The process can be repeated until all objects have been classified.
    • 图像由基于感测特征的分类器处理以生成分配给类的对象的列表。 选择最突出的对象(那些分类最可靠的对象)用于范围估计和插值。 基于范围估计和内插,感测到的特征被转换为每个对象的物理特征。 接下来,该对象的子集然后通过基于物理特征的分类器来运行,该分类器重新分类对象。 接下来,通过范围估计和插值,感测特征到物理特征转换和基于物理特征的分类的过程重新运行对象及其范围估计,以不断提高分类的可靠性 作为范围估计。 当可靠性达到预定的置信阈值时,迭代停止。 在优选实施例中,选择具有相同图像中的下一个最高突出的对象的下一个子集,并重复整个迭代过程。 这组迭代将包括评估对象的第一和第二子集。 可以重复该过程,直到所有对象都被分类为止。
    • 4. 发明授权
    • Infrared and visible fusion face recognition system
    • 红外和可见融合面识别系统
    • US07602942B2
    • 2009-10-13
    • US10987806
    • 2004-11-12
    • Michael E. BazakosVassilios MorellasYunqian Ma
    • Michael E. BazakosVassilios MorellasYunqian Ma
    • G06K9/00
    • G06K9/00255G01J3/36
    • A face detection and recognition system having several arrays imaging a scene in the infrared and visible spectrums. The system may use weighted subtracting and thresholding to distinguish human skin in a sensed image. A feature selector may locate a face in the image. The image may be cropped with a frame or border incorporating essentially only the face. The border may be superimposed on images from an infrared imaging array and the visible imaging array. Sub-images containing the face may be extracted from within the border on the infrared and visible images, respectively, and compared with a database of face information to attain recognition of the face. Confidence levels of recognition for infrared and visible imaged faces may be established. A resultant confidence level of recognition may be determined from these confidence levels. Infrared lighting may be used as needed to illuminate the scene.
    • 一种面部检测和识别系统,其具有若干阵列,用于对红外和可见光谱中的场景进行成像。 系统可以使用加权减法和阈值来区分感测图像中的人皮肤。 特征选择器可以在图像中定位一个面。 图像可以用基本上仅包含脸部的框架或边框来裁剪。 边界可以叠加在来自红外成像阵列和可见成像阵列的图像上。 可以分别从红外线和可见图像的边界内提取包含脸部的子图像,并与面部信息的数据库进行比较以获得脸部的识别。 可以确定红外和可见成像面的识别水平。 可以从这些置信水平确定所得到的识别水平。 可以根据需要使用红外线照明来照亮场景。
    • 6. 发明授权
    • Automated activity detection using supervised learning
    • 使用监督学习的自动活动检测
    • US07881537B2
    • 2011-02-01
    • US11343658
    • 2006-01-31
    • Yunqian MaMichael E. Bazakos
    • Yunqian MaMichael E. Bazakos
    • G06K9/46G06K9/62G06K9/00
    • G06K9/00348
    • In an embodiment, one or more sequences of learning video data is provided. The learning video sequences include an action. One or more features of the action are extracted from the one or more sequences of learning video data. Thereafter, a sequence of operational video data is received, and the one or more features of the action from the sequence of operational video data is extracted. A comparison is then made between the extracted one or more features of the action from the one or more sequences of learning video data and the one or more features of the action from the sequence of operational video data. In an embodiment, this comparison allows the determination of whether the action is present in the operational video data.
    • 在一个实施例中,提供学习视频数据的一个或多个序列。 学习视频序列包括动作。 从学习视频数据的一个或多个序列中提取动作的一个或多个特征。 此后,接收一系列操作视频数据,并提取来自操作视频数据序列的动作的一个或多个特征。 然后,从所述学习视频数据的一个或多个序列以及来自所述操作视频数据序列的所述动作的所述一个或多个特征,提取所述动作的一个或多个特征之间的比较。 在一个实施例中,该比较允许确定动作是否存在于操作视频数据中。
    • 7. 发明授权
    • Multi-spectral fusion for video surveillance
    • 用于视频监控的多光谱融合
    • US07613360B2
    • 2009-11-03
    • US11345203
    • 2006-02-01
    • Yunqian MaMichael E. Bazakos
    • Yunqian MaMichael E. Bazakos
    • G06K9/36G06K9/00
    • G06K9/6289G06K9/00771G06K9/4652H04N5/332
    • A multi-spectral imaging surveillance system and method in which a plurality of imaging cameras is associated with a data-processing apparatus. A module can be provided, which resides in a memory of said data-processing apparatus. The module performs fusion of a plurality images respectively generated by varying imaging cameras among said plurality of imaging cameras. Fusion of the images is based on a plurality of parameters indicative of environmental conditions in order to achieve enhanced imaging surveillance thereof. The final fused images are the result of two parts: an image fusion portion, and a knowledge representation part. For the final fusion, many operators can be utilized, which can be applied between the image fusion result and the knowledge representation portion.
    • 一种多光谱成像监视系统和方法,其中多个成像摄像机与数据处理设备相关联。 可以提供一个位于所述数据处理装置的存储器中的模块。 所述模块执行由所述多个成像照相机中的各种成像照相机分别产生的多个图像的融合。 图像的融合基于指示环境条件的多个参数,以便实现其增强的成像监视。 最终的融合图像是两部分的结果:图像融合部分和知识表示部分。 对于最终融合,可以利用许多操作者,这可以在图像融合结果和知识表示部分之间应用。
    • 8. 发明授权
    • Infrared face detection and recognition system
    • 红外面部检测和识别系统
    • US07469060B2
    • 2008-12-23
    • US10987368
    • 2004-11-12
    • Michael E. BazakosVassilios MorellasAndrew JohnsonYunqian Ma
    • Michael E. BazakosVassilios MorellasAndrew JohnsonYunqian Ma
    • G06K9/34
    • H04N5/23219G06K9/00255
    • A face detection and recognition system having several arrays imaging a scene at different bands of the infrared spectrum. The system may use weighted subtracting and thresholding to distinguish human skin in a sensed image. A feature selector may locate a face in the image. The face may be framed or the image cropped with a frame or border to incorporate essentially only the face. The border may be superimposed on an image direct from an imaging array. A sub-image containing the face may be extracted from within the border and compared with a database of face information to attain recognition of the face. A level of recognition of the face may be established. Infrared lighting may be used as needed to illuminate the scene.
    • 具有若干阵列的人脸检测和识别系统对红外光谱的不同频带的场景进行成像。 系统可以使用加权减法和阈值来区分感测图像中的人皮肤。 特征选择器可以在图像中定位一个面。 脸部可能被框起来,或者用框架或边框裁剪的图像基本上只包括脸部。 边界可以直接从成像阵列叠加在图像上。 可以从边框内提取包含脸部的子图像,并与面部信息的数据库进行比较以获得脸部的识别。 可以建立对面部的认可程度。 可以根据需要使用红外线照明来照亮场景。
    • 9. 发明申请
    • Anomaly detection in a video system
    • 视频系统中的异常检测
    • US20080031491A1
    • 2008-02-07
    • US11498923
    • 2006-08-03
    • Yunqian MaMichael E. BazakosKwong Wing Au
    • Yunqian MaMichael E. BazakosKwong Wing Au
    • G06K9/00G06K9/46G06K9/62G06K9/66
    • G06K9/00771G06K9/6284G08B13/19613
    • In an embodiment, a video processor is configured to identify anomalous or abnormal behavior. A hierarchical behavior model based on the features of the complement of the abnormal behavior of interest is developed. For example, if the abnormal behavior is stealing or shoplifting, a model is developed for the actions of normal shopping behavior (i.e., not stealing or not shoplifting). Features are extracted from video data and applied to an artificial intelligence construct such as a dynamic Bayesian network (DBN) to determine if the normal behavior is present in the video data (i.e, the complement of the abnormal behavior). If the DBN indicates that the extracted features depart from the behavior model (the complement of the abnormal behavior), then the presence of the abnormal behavior in the video data may be assumed.
    • 在一个实施例中,视频处理器被配置为识别异常或异常行为。 开发了基于异常行为的补充特征的分层行为模型。 例如,如果异常行为是偷窃或偷窃,则为正常购物行为(即不偷窃或不偷窃)的行为开发出一个模型。 从视频数据提取特征并将其应用于诸如动态贝叶斯网络(DBN)的人造智能结构,以确定视频数据中是否存在正常行为(即,异常行为的补充)。 如果DBN指示提取的特征离开行为模型(异常行为的补充),则可以假设视频数据中的异常行为的存在。