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21. 发明申请

US20100271905A1 WEAPON IDENTIFICATION USING ACOUSTIC SIGNATURES ACROSS VARYING CAPTURE CONDITIONS 有权
标题翻译：使用声音识别的武器识别符合各种不同的捕获条件
公开(公告)号：US20100271905A1
公开(公告)日：2010-10-28
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示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。

22. 发明授权

US07409407B2 Multimedia event detection and summarization 失效
标题翻译：多媒体事件检测与总结
公开(公告)号：US07409407B2
公开(公告)日：2008-08-05
申请号：US10840824
申请日：2004-05-07
申请人： Regunathan Radhakrishnan , Ajay Divakaran
发明人： Regunathan Radhakrishnan , Ajay Divakaran
IPC分类号： G06F17/30 , G06F17/00
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。针对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。

23. 发明申请

US20080124042A1 Method and system for video segmentation 有权
标题翻译：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。

24. 发明申请

US20060075346A1 Video presentation using compositional structures 审中-公开
标题翻译：使用组合结构的视频演示
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。

25. 发明申请

US20050154987A1 System and method for recording and reproducing multimedia 审中-公开
标题翻译：用于录制和再现多媒体的系统和方法
公开(公告)号：US20050154987A1
公开(公告)日：2005-07-14
申请号：US10757138
申请日：2004-01-14
申请人： Isao Otsuka , Ajay Divakaran , Masaharu Ogawa , Kazuhiko Nakane
发明人： Isao Otsuka , Ajay Divakaran , Masaharu Ogawa , Kazuhiko Nakane
IPC分类号： G06F15/00 , G06F17/30 , G10L19/00 , G11B27/00 , G11B27/28 , H04N5/91 , H04N9/804 , H04N9/82
CPC分类号： H04N9/8205 , G06F16/71 , G06F16/739 , G06F16/7834 , G11B27/28 , H04N9/8042 , H04N21/42646 , H04N21/4325 , H04N21/4394 , H04N21/44008 , H04N21/4508 , H04N21/4542 , H04N21/84
摘要： A system and method summarizes multimedia stored in a compressed multimedia file partitioned into a sequence of segments, where the content of the multimedia is, for example, video signals, audio signals, text, and binary data. An associated metadata file includes index information and an importance level for each segment. The importance information is continuous over as closed interval. An importance level threshold is selected in the closed interval, and only segments of the multimedia having a particular importance level greater than the importance level threshold are reproduced.
摘要翻译：系统和方法总结了存储在被分割成段序列的压缩多媒体文件中的多媒体，其中多媒体的内容是例如视频信号，音频信号，文本和二进制数据。关联的元数据文件包括索引信息和每个段的重要性级别。重要性信息作为闭合间隔连续。在闭合间隔中选择重要性级别阈值，并且仅再现具有大于重要性级别阈值的特定重要性级别的多媒体段。

26. 发明申请

US20050125223A1 Audio-visual highlights detection using coupled hidden markov models 审中-公开
标题翻译：使用耦合的隐马尔可夫模型的视听亮点检测
公开(公告)号：US20050125223A1
公开(公告)日：2005-06-09
申请号：US10729164
申请日：2003-12-05
申请人： Ajay Divakaran , Ziyou Xiong , Regunathan Radhakrishnan
发明人： Ajay Divakaran , Ziyou Xiong , Regunathan Radhakrishnan
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。

27. 发明授权

US08385154B2 Weapon identification using acoustic signatures across varying capture conditions 有权
标题翻译：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。

28. 发明申请

US20120106800A1 3-D MODEL BASED METHOD FOR DETECTING AND CLASSIFYING VEHICLES IN AERIAL IMAGERY 有权
标题翻译：用于在航空影像中检测和分类车辆的基于3-D模型的方法
公开(公告)号：US20120106800A1
公开(公告)日：2012-05-03
申请号：US12913861
申请日：2010-10-28
申请人： Saad Masood Khan , Hui Cheng , Dennis Lee Matthies , Harpreet Singh Sawhney , Sang-Hack Jung , Chris Broaddus , Bogdan Calin Mihai Matei , Ajay Divakaran
发明人： Saad Masood Khan , Hui Cheng , Dennis Lee Matthies , Harpreet Singh Sawhney , Sang-Hack Jung , Chris Broaddus , Bogdan Calin Mihai Matei , Ajay Divakaran
IPC分类号： G06K9/68
CPC分类号： G06K9/00785 , B64C39/024 , B64C2201/123 , B64D47/08 , G06K9/00651 , G06K9/4671 , G06K9/6269 , G06K9/6282 , G06T7/11 , G06T7/20 , G06T2207/10012
摘要： A computer implemented method for determining a vehicle type of a vehicle detected in an image is disclosed. An image having a detected vehicle is received. A number of vehicle models having salient feature points is projected on the detected vehicle. A first set of features derived from each of the salient feature locations of the vehicle models is compared to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a set of positive match scores (p-scores) and a set of negative match scores (n-scores). The detected vehicle is classified as one of the vehicle models models based at least in part on the set of p-scores and the set of n-scores.
摘要翻译：公开了一种用于确定在图像中检测到的车辆的车辆类型的计算机实现的方法。接收具有检测到的车辆的图像。在检测到的车辆上投影出具有显着特征点的多个车辆模型。将来自车辆模型的每个突出特征位置的第一组特征与从所检测到的车辆的相应突出特征位置导出的第二组特征进行比较，以形成一组正的匹配得分（p分数）和一组负面比赛得分（n分）。检测到的车辆至少部分地基于p分数集合和n分数集合被分类为车辆模型模型之一。

29. 发明申请

US20100208941A1 ACTIVE COORDINATED TRACKING FOR MULTI-CAMERA SYSTEMS 有权
标题翻译：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。

30. 发明授权

US07558809B2 Task specific audio classification for identifying video highlights 有权
标题翻译：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。

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