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公开(公告)号：US07327885B2
公开(公告)日：2008-02-05
申请号：US10610467
申请日：2003-06-30
CPC分类号： H04N21/4394 , G06K9/00711 , H04H60/37 , H04H60/58 , H04H60/59 , H04N21/44008
摘要： A method detects short term, unusual events in a video. First, features are extracted features from the audio and the video portions of the video. Segments of the video are labeled according to the features. A global sliding window is applied to the labeled segments to determine global characteristics over time, while a local sliding window is applied only to the labeled segments of the global sliding window to determine local characteristic over time. The local window is substantially shorter in time than the global window. A distance between the global and local characteristic is measured to determine occurrences of the unusual short time events.
摘要翻译： 一种方法可以检测视频中的短期，异常事件。 首先，功能是从视频的音频和视频部分中提取的特征。 视频的片段根据功能进行标注。 将全局滑动窗口应用于标记的段以确定随时间的全局特性，而局部滑动窗口仅应用于全局滑动窗口的标记段以确定随时间的局部特性。 本地窗口在时间上比全局窗口短得多。 测量全局和局部特性之间的距离，以确定异常短时间事件的发生。
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公开(公告)号：US20080033674A1
公开(公告)日：2008-02-07
申请号：US11498289
申请日：2006-08-01
CPC分类号： G05B23/0254 , F24F11/30 , F24F11/52 , F24F2110/00
摘要： A method and system detects and diagnoses faults in heating, ventilating and air conditioning (HVAC) equipment. Internal state variables of the HVAC equipment are measured under external driving conditions. Expected internal state variables are predicted for the HVAC equipment operating under the external driving conditions using a locally weighted regression model. Features are determined of the HVAC based on differences between the measured and predicted state variables. The features are classified to determine a condition of the HVAC equipment.
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公开(公告)号：US07444251B2
公开(公告)日：2008-10-28
申请号：US11498289
申请日：2006-08-01
CPC分类号： G05B23/0254 , F24F11/30 , F24F11/52 , F24F2110/00
摘要： A method and system detects and diagnoses faults in heating, ventilating and air conditioning (HVAC) equipment. Internal state variables of the HVAC equipment are measured under external driving conditions. Expected internal state variables are predicted for the HVAC equipment operating under the external driving conditions using a locally weighted regression model. Features are determined of the HVAC based on differences between the measured and predicted state variables. The features are classified to determine a condition of the HVAC equipment.
摘要翻译： 一种方法和系统检测和诊断加热，通风和空调（HVAC）设备中的故障。 HVAC设备的内部状态变量在外部驾驶条件下测量。 对于在外部驾驶条件下使用局部加权回归模型运行的暖通空调设备，预测内部状态变量。 特征根据测量和预测状态变量之间的差异确定HVAC。 这些特征被分类以确定HVAC设备的状况。
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公开(公告)号：US07110458B2
公开(公告)日：2006-09-19
申请号：US09845009
申请日：2001-04-27
IPC分类号： H04N7/18
CPC分类号： H04N19/186 , G06F17/30802 , G06F17/30811 , G06F17/30814 , G06F17/30843 , G06K9/00711 , G11B27/28 , H04N19/51
摘要： A method measures an intensity of motion activity in a compressed video. The intensity of the motion activity is used to partition the video into segments of equal cumulative motion activity. Key-frames are then selected from each segments. The selected key-frames are concatenated in temporal order to form a summary of the video.
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公开(公告)号：US20060059120A1
公开(公告)日：2006-03-16
申请号：US10928829
申请日：2004-08-27
IPC分类号： G06F17/30
CPC分类号： G06K9/00711 , G06F16/739 , G06F16/7834 , G06F16/785 , G06K9/6293
摘要： A method identifies highlight segments in a video including a sequence of frames. Audio objects are detected to identify frames associated with audio events in the video, and visual objects are detected to identify frames associated with visual events. Selected visual objects are matched with an associated audio object to form an audio-visual object only if the selected visual object matches the associated audio object, the audio-visual object identifying a candidate highlight segment. The candidate highlight segments are further refined, using low level features, to eliminate false highlight segments.
摘要翻译： 一种方法识别包括帧序列的视频中的高亮段。 检测音频对象以识别与视频中的音频事件相关联的帧，并且检测可视对象以识别与视觉事件相关联的帧。 仅当所选择的视觉对象与相关联的音频对象匹配时，所选择的可视对象与相关联的音频对象匹配才能形成视听对象，该视听对象识别候选高亮段。 候选高亮段进一步细化，使用低级特征，消除假高光段。
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公开(公告)号：US20060075346A1
公开(公告)日：2006-04-06
申请号：US10951192
申请日：2004-09-27
申请人： Tom Lanning , Ajay Divakaran , Kadir Peker , Regunathan Radhakrishnan , Ziyou Xiong , Clifton Forlines
发明人： Tom Lanning , Ajay Divakaran , Kadir Peker , Regunathan Radhakrishnan , Ziyou Xiong , Clifton Forlines
IPC分类号： G11B27/00
CPC分类号： G11B19/025 , G11B27/105 , G11B27/107 , G11B27/11 , G11B27/28 , G11B27/34 , G11B2220/20 , G11B2220/65 , G11B2220/90 , H04N5/85 , H04N9/8042 , H04N9/8233 , H04N21/42646 , H04N21/4312 , H04N21/4314 , H04N21/4325 , H04N21/812 , H04N21/84 , H04N21/8456
摘要： A method presents a video according to compositional structures associated with the video. Each compositional structure has a label, and multiple segments that can be organized temporally or hierarchically. A particular compositional structure is selected with a remote controller, and the video is presented by a playback controller on a display device according to the compositional structure.
摘要翻译： 一种方法根据与视频相关联的组合结构呈现视频。 每个组成结构都有一个标签，并且可以在时间上或分层上组织的多个片段。 利用遥控器选择特定的组成结构，根据组成结构，视频由显示设备上的播放控制器呈现。
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7.发明申请公开(公告)号：US20050125223A1
公开(公告)日：2005-06-09
申请号：US10729164
申请日：2003-12-05
IPC分类号： G06F17/30 , G06K9/00 , G06K9/20 , G06K9/62 , G06T7/00 , G10L11/00 , G10L15/00 , G10L15/04 , G10L15/10 , G10L15/26 , G10L17/00 , G10L19/12 , H04N5/76
CPC分类号： G06K9/00711 , G06F16/739 , G06F16/7834 , G06F16/786 , G06K9/6297
摘要： A method uses probabilistic fusion to detect highlights in videos using both audio and visual information. Specifically, the method uses coupled hidden Markov models (CHMMs). Audio labels are generated using audio classification via Gaussian mixture models (GMMs), and visual labels are generated by quantizing average motion vector magnitudes. Highlights are modeled using discrete-observation CHMMs trained with labeled videos. The CHMMs have better performance than conventional hidden Markov models (HMMs) trained only on audio signals, or only on video frames.
摘要翻译： 一种方法使用概率融合来检测使用音频和视觉信息的视频中的高光。 具体来说，该方法使用耦合的隐马尔可夫模型（CHMM）。 使用高斯混合模型（GMM）的音频分类生成音频标签，并且通过量化平均运动矢量幅度来生成视觉标签。 亮点是使用用标记视频训练的离散观察CHMM进行建模。 CHMM具有比仅在音频信号上训练的传统隐马尔可夫模型（HMM）更好的性能，或仅在视频帧上训练。
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公开(公告)号：US20070162924A1
公开(公告)日：2007-07-12
申请号：US11326818
申请日：2006-01-06
CPC分类号： H04N21/4394 , G06K9/00718 , G10L25/00 , H04N21/235 , H04N21/435 , H04N21/8456 , Y10S707/99942 , Y10S707/99943 , Y10S707/99945 , Y10S707/99948
摘要： A method classifies segments of a video using an audio signal of the video and a set of classes. Selected classes of the set are combined as a subset of important classes, the subset of important classes being important for a specific highlighting task, the remaining classes of the set are combined as a subset of other classes. The subset of important classes and classes are trained with training audio data to form a task specific classifier. Then, the audio signal can be classified using the task specific classifier as either important or other to identify highlights in the video corresponding to the specific highlighting task. The classified audio signal can be used to segment and summarize the video.
摘要翻译： 一种方法使用视频的音频信号和一组类来分类视频的片段。 集合的所选类被组合为重要类的子集，重要类的子集对于特定高亮任务是重要的，集合的剩余类被组合为其他类的子集。 通过训练音频数据来训练重要类和类的子集，以形成任务特定的分类器。 然后，可以使用任务专用分类器将音频信号分类为重要的或其他的，以识别与特定突出显示任务相对应的视频中的亮点。 分类音频信号可用于分割和总结视频。
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公开(公告)号：US20050249412A1
公开(公告)日：2005-11-10
申请号：US10840824
申请日：2004-05-07
IPC分类号： H04N5/91 , G06F7/00 , G06F17/30 , G06K9/00 , G06K9/34 , G06K9/54 , G06K9/62 , G06T7/20 , G10L11/00 , G10L15/10 , G10L15/14
CPC分类号： G06F17/30787 , G06F17/30802 , G06F17/30808 , G06F17/30811 , G06F17/30843 , G06K9/00711 , Y10S707/99943 , Y10S707/99945
摘要： A method detects events in multimedia. Features are extracted from the multimedia. The features are sampled using a sliding window to obtain samples. A context model is constructed for each sample. An affinity matrix is determined from the models and a commutative distance metric between each pair of context models. A second generation eigenvector is determined for the affinity matrix, and the samples are then clustered into events according to the second generation eigenvector.
摘要翻译： 一种方法来检测多媒体中的事件。 功能从多媒体提取。 使用滑动窗口对特征进行采样以获得样品。 为每个样本构建上下文模型。 从模型和每对上下文模型之间的交换距离度量确定亲和度矩阵。 对于亲和度矩阵确定第二代特征向量，然后根据第二代特征向量将样本聚类成事件。
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公开(公告)号：US06823011B2
公开(公告)日：2004-11-23
申请号：US09997497
申请日：2001-11-19
IPC分类号： H04N712
CPC分类号： H04N19/142 , G06K9/00261 , G06K9/00335 , G06K9/00744 , H04N19/179 , H04N19/46 , H04N19/503 , H04N19/517 , H04N19/87
摘要： A method detects an unusual event in a video. Motion vectors are extracted from each frame in a video acquired by a camera of a scene. Zero run-length parameters are determined for each frame from the motion vectors. The zero run-length parameters are summed over predetermined time intervals of the video, and a distance is determined between the sum of the zero run-lengths of a current time interval and the sum of the zero run-lengths of a previous time interval. Then, the unusual event is detected if the distance is greater than a predetermined threshold.
摘要翻译： 一种方法检测视频中的异常事件。 在由场景的相机获取的视频中的每个帧中提取运动矢量。 从运动矢量为每个帧确定零游程长度参数。 零游程长度参数在视频的预定时间间隔上相加，并且在当前时间间隔的零游程长度与前一时间间隔的零游程长度之和之间确定距离。 然后，如果距离大于预定阈值，则检测到异常事件。
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