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    Method and system for video segmentation
    11.
    发明申请
    Method and system for video segmentation 有权
    视频分割方法和系统

    公开(公告)号：US20080124042A1

    公开(公告)日：2008-05-29

    申请号：US11593897

    申请日：2006-11-07

    申请人： Ajay Divakaran ,  Feng Niu ,  Naveen Goela

    发明人： Ajay Divakaran ,  Feng Niu ,  Naveen Goela

    IPC分类号： H04N5/93

    CPC分类号： G06K9/00711 ,  G06F17/30787 ,  G10L25/48

    摘要： A method segments a video. Audio frames of the video are classified with labels. Dominant labels are assigned to successive time intervals of consecutive labels. A semantic description is constructed for sliding time windows of the successive time intervals, in which the sliding time windows overlap in time, and the semantic description for each time window is a transition matrix determined from the dominant labels of the time intervals. A marker is determined from the transition matrices, in which a frequency of occurrence of the marker is between a low frequency threshold and a high frequency threshold. Then, the video is segmented at the locations of the markers.

    摘要翻译： 一种方法分割视频。 视频的音频帧被分类为标签。 主导标签分配给连续标签的连续时间间隔。 对于连续时间间隔的滑动时间窗口构成语义描述，其中滑动时间窗口在时间上重叠，并且每个时间窗口的语义描述是从时间间隔的主要标签确定的转换矩阵。 从标记的出现频率在低频阈值和高频阈值之间的转移矩阵确定标记。 然后，视频在标记的位置被分割。

    Methods of feature extraction of video sequences
    15.
    发明授权
    Methods of feature extraction of video sequences 失效
    视频序列特征提取方法

    公开(公告)号：US06618507B1

    公开(公告)日：2003-09-09

    申请号：US09236838

    申请日：1999-01-25

    申请人： Ajay Divakaran ,  Huifang Sun ,  Hiroshi Ito

    发明人： Ajay Divakaran ,  Huifang Sun ,  Hiroshi Ito

    IPC分类号： G06K946

    CPC分类号： G06K9/00744 ,  G06F17/30811

    摘要： This invention relates to methods of feature extraction from MPEG-2 and MPEG-4 compressed video sequences. The spatio-temporal compression complexity of video sequences is evaluated for feature extraction by inspecting the compressed bitstream and the complexity is used as a descriptor of the spatio-temporal characteristics of the video sequence. The spatio-temporal compression complexity measure is used as a matching criterion and can also be used for absolute indexing. Feature extraction can be accomplished in conjunction with scene change detection techniques and the combination has reasonable accuracy and the advantage of high simplicity since it is based on entropy decoding of signals in compressed form and does not require computationally expensive inverse Discrete Cosine Transformation (DCT).

    摘要翻译： 本发明涉及从MPEG-2和MPEG-4压缩视频序列中提取特征的方法。 通过检查压缩比特流来评估视频序列的时空压缩复杂度，并将复杂度用作视频序列的时空特征的描述符。 时空压缩复杂度测量用作匹配标准，也可用于绝对索引。 特征提取可以结合场景变化检测技术来实现，并且组合具有合理的精度和高简单性的优点，因为它是基于压缩形式的信号的熵解码，并且不需要计算上昂贵的反离散余弦变换（DCT）。

    Descriptor for spatial distribution of motion activity in compressed video
    16.
    发明授权
    Descriptor for spatial distribution of motion activity in compressed video 有权
    压缩视频中运动活动空间分布的描述符

    公开(公告)号：US06600784B1

    公开(公告)日：2003-07-29

    申请号：US09496707

    申请日：2000-02-02

    申请人： Ajay Divakaran ,  Kadir A. Peker ,  Huifang Sun

    发明人： Ajay Divakaran ,  Kadir A. Peker ,  Huifang Sun

    IPC分类号： H04N712

    CPC分类号： G06F17/30781 ,  G06K9/00335 ,  G06K9/00711 ,  G06T7/215 ,  G06T2207/10016 ,  G06T2207/20021

    摘要： A method describes motion activity in a video sequence. A motion activity matrix is determined for the video sequence. A threshold for the motion activity matrix is determined. Connected regions of motion vectors at least equal to the threshold are identified and measured for size. A histogram of the distribution of the sizes of the connected areas is constructed for the entire video sequence. The histogram is normalized to characterize the spatial distribution of the video sequence in a motion activity descriptor.

    摘要翻译： 一种方法描述视频序列中的运动活动。 为视频序列确定运动活动矩阵。 确定运动活动矩阵的阈值。 至少等于阈值的运动矢量的连接区域被识别并测量尺寸。 为整个视频序列构建连接区域的大小分布的直方图。 将直方图归一化以表征运动活动描述符中的视频序列的空间分布。

    Weapon identification using acoustic signatures across varying capture conditions
    17.
    发明授权
    Weapon identification using acoustic signatures across varying capture conditions 有权
    使用声学签名的武器识别在不同的捕获条件下

    公开(公告)号：US08385154B2

    公开(公告)日：2013-02-26

    申请号：US12766219

    申请日：2010-04-23

    申请人： Saad Khan ,  Ajay Divakaran ,  Harpreet Singh Sawhney

    发明人： Saad Khan ,  Ajay Divakaran ,  Harpreet Singh Sawhney

    IPC分类号： G01S3/80

    CPC分类号： G10L25/48

    摘要： A computer implemented method for automatically detecting and classifying acoustic signatures across a set of recording conditions is disclosed. A first acoustic signature is received. The first acoustic signature is projected into a space of a minimal set of exemplars of acoustic signature types derived from a larger set of exemplars using a wrapper method. At least one vector distance is calculated between the projected acoustic signature and each exemplar of the minimal set of exemplars. An exemplar is selected from the minimal set of exemplars having the smallest vector distance to the projected acoustic signature as a class corresponding to and classifying the first acoustic signature. The first acoustic signature and the plurality of acoustic signatures may correspond to one of gunshots, musical instruments, songs, and speech. The minimal set of exemplars may correspond to a hierarchy of acoustic signature types.

    摘要翻译： 公开了一种用于在一组记录条件下自动检测和分类声学签名的计算机实现的方法。 接收到第一个声学签名。 第一声​​学签名被投影到使用包装方法从更大的样本集合导出的声学签名类型的最小样本集合的空间中。 在投影的声学特征与最小样本集的每个样本之间计算至少一个矢量距离。 从具有与投影的声学签名的最小向量距离的最小样本集合中选择一个范例作为对应于和分类第一声学签名的类别。 第一声​​学签名和多个声学签名可以对应于枪声，乐器，歌曲和语音之一。 最小的一组样本可以对应于声学签名类型的层级。

    ACTIVE COORDINATED TRACKING FOR MULTI-CAMERA SYSTEMS
    19.
    发明申请
    ACTIVE COORDINATED TRACKING FOR MULTI-CAMERA SYSTEMS 有权
    主动协调跟踪多摄像机系统

    公开(公告)号：US20100208941A1

    公开(公告)日：2010-08-19

    申请号：US12704189

    申请日：2010-02-11

    申请人： Christopher P. Broaddus ,  Thomas Germano ,  Nicholas Vandervalk ,  Shunguang Wu ,  Ajay Divakaran ,  Harpreet Singh Sawhney

    发明人： Christopher P. Broaddus ,  Thomas Germano ,  Nicholas Vandervalk ,  Shunguang Wu ,  Ajay Divakaran ,  Harpreet Singh Sawhney

    IPC分类号： G06T7/00

    CPC分类号： G01S13/867 ,  G01S3/7864 ,  G01S13/726 ,  G06T7/194 ,  G06T7/292 ,  G06T2207/20076 ,  G06T2207/30232 ,  G08B13/19608 ,  G08B13/19645 ,  G08B13/19697

    摘要： A method and system for coordinated tracking of objects is disclosed. A plurality of images is received from a plurality of nodes, each node comprising at least one image capturing device. At least one target in the plurality of images is identified to produce at least one local track corresponding to each of the plurality of nodes having the at least one target in its field of view. The at least one local track corresponding to each of the plurality of nodes is fused according to a multi-hypothesis tracking method to produce at least one fused track corresponding to the at least one target. At least one of the plurality of nodes is assigned to track the at least one target based on minimizing at least one cost function comprising a cost matrix using the k-best algorithm for tracking at least one target for each of the plurality of nodes. The at least one fused track is sent to the at least one of the plurality of nodes assigned to track the at least one target based on the at least one fused track.

    摘要翻译： 公开了一种协调跟踪物体的方法和系统。 从多个节点接收多个图像，每个节点包括至少一个图像捕获装置。 多个图像中的至少一个目标被识别以产生与其视场中具有至少一个目标的多个节点中的每一个对应的至少一个本地轨迹。 根据多假设跟踪方法将与多个节点中的每一个相对应的至少一个本地轨道融合以产生至少一个对应于该至少一个目标的融合轨道。 所述多个节点中的至少一个被分配以跟踪所述至少一个目标，所述至少一个目标基于最小化至少一个成本函数，所述至少一个成本函数包括使用所述k-最佳算法的成本矩阵，用于跟踪所述多个节点中的每个节点的至少一个目 至少一个融合轨道被发送到被分配以基于至少一个融合轨道跟踪至少一个目标的多个节点中的至少一个。

    Task specific audio classification for identifying video highlights
    20.
    发明授权
    Task specific audio classification for identifying video highlights 有权
    用于识别视频亮点的任务特定音频分类

    公开(公告)号：US07558809B2

    公开(公告)日：2009-07-07

    申请号：US11326818

    申请日：2006-01-06

    申请人： Regunathan Radhakrishnan ,  Michael Siracusa ,  Ajay Divakaran

    发明人： Regunathan Radhakrishnan ,  Michael Siracusa ,  Ajay Divakaran

    IPC分类号： G06F17/00

    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。