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公开(公告)号：US09489582B2
公开(公告)日：2016-11-08
申请号：US14534790
申请日：2014-11-06
Applicant: Xerox Corporation , The Penn State Research Foundation
Inventor： Raja Bala , Aaron M. Burry , Vishal Monga , Xuan Mo
IPC: G06K9/00
CPC classification number: G06K9/00785 , G06K9/00335
Abstract: Methods, systems, and processor-readable media for video anomaly detection based upon a sparsity model. A video input can be received and two or more diverse descriptors of an event can be computed from the video input. The descriptors can be combined to form an event matrix. A sparse reconstruction of the event matrix can be performed with respect to an over complete dictionary of training events represented by the diverse descriptors. A step can then be performed to determine if the event is anomalous by computing an outlier rejection measure on the sparse reconstruction.
Abstract translation: 基于稀疏模型的视频异常检测的方法，系统和处理器可读介质。 可以接收视频输入，并且可以从视频输入计算事件的两个或多个不同描述符。 描述符可以组合形成一个事件矩阵。 相对于由不同描述符表示的训练事件的完整字典，可以执行事件矩阵的稀疏重建。 然后可以执行一个步骤，以通过计算稀疏重建的异常值拒绝测量来确定事件是否是异常的。
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Patent Agency Ranking
2.发明授权Dictionary design for computationally efficient video anomaly detection via sparse reconstruction techniques 有权通过稀疏重建技术进行计算高效视频异常检测的词典设计公开(公告)号：US09098749B2
公开(公告)日：2015-08-04
申请号：US13827222
申请日：2013-03-14
Applicant: Xerox Corporation
Inventor： Raja Bala , Zhigang Fan , Aaron Michael Burry , Jose Antonio Rodriguez-Serrano , Vishal Monga , Xuan Mo
CPC classification number: G06K9/00771 , G06K9/6249
Abstract: Methods, systems, and processor-readable media for pruning a training dictionary for use in detecting anomalous events from surveillance video. Training samples can be received, which correspond to normal events. A dictionary can then be constructed, which includes two or more classes of normal events from the training samples. Sparse codes are then generated for selected training samples with respect to the dictionary derived from the two or more classes of normal events. The size of the dictionary can then be reduced by removing redundant dictionary columns from the dictionary via analysis of the sparse codes. The dictionary is then optimized to yield a low reconstruction error and a high-interclass discriminability.
Abstract translation: 用于修剪用于检测来自监视视频的异常事件的训练词典的方法，系统和处理器可读介质。 可以收到培训样本，对应于正常事件。 然后可以构建字典，其包括来自训练样本的两个或更多类的正常事件。 然后针对从两个或多个正常事件类派生的字典为选定的训练样本生成稀疏码。 然后可以通过对稀疏代码的分析从字典中删除冗余字典列来减少字典的大小。 然后对该字典进行优化，以产生低重构误差和高阶间的可辨别性。
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公开(公告)号：US09317780B2
公开(公告)日：2016-04-19
申请号：US14326635
申请日：2014-07-09
Applicant: Xerox Corporation
Inventor： Raja Bala , Zhigang Fan , Aaron Burry , Vishal Monga , Xuan Mo
CPC classification number: G06K9/6249 , G06K9/00771
Abstract: Methods and systems for detecting anomalies in transportation related video footage. In an offline training phase, receiving video footage of a traffic location can be received. Also, in an offline training phase, event encodings can be extracted from the video footage and collected or compiled into a training dictionary. One or more input video sequences captured at the traffic location or a similar traffic location can be received in an online detection phase. Then, an event encoding corresponding to the input video sequence can be extracted. The event encoding can be reconstructed with a low rank sparsity prior model applied with respect to the training dictionary. The reconstruction error between actual and reconstructed event encodings can then be computed in order to determine if an event thereof is anomalous by comparing the reconstruction error with a threshold.
Abstract translation: 检测交通相关影像异常的方法和系统。 在离线训练阶段，可以接收到接收到交通位置的录像带。 此外，在离线训练阶段，可以从视频素材中提取事件编码，并将其收集或编译成训练词典。 在在线检测阶段可以接收在业务位置或类似业务位置处捕获的一个或多个输入视频序列。 然后，可以提取与输入视频序列相对应的事件编码。 可以使用相对于训练词典应用的低秩稀疏性先验模型来重构事件编码。 然后可以计算实际和重建事件编码之间的重建误差，以便通过将重建误差与阈值进行比较来确定其事件是否是异常的。
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公开(公告)号：US20150213323A1
公开(公告)日：2015-07-30
申请号：US14534790
申请日：2014-11-06
Applicant: Xerox Corporation , The Penn State Research Foundation
Inventor： Raja Bala , Aaron M. Burry , Vishal Monga , Xuan Mo
CPC classification number: G06K9/00785 , G06K9/00335
Abstract: Methods, systems, and processor-readable media for video anomaly detection based upon a sparsity model. A video input can be received and two or more diverse descriptors of an event can be computed from the video input. The descriptors can be combined to form an event matrix. A sparse reconstruction of the event matrix can be performed with respect to an over complete dictionary of training events represented by the diverse descriptors. A step can then be performed to determine if the event is anomalous by computing an outlier rejection measure on the sparse reconstruction.
Abstract translation: 基于稀疏模型的视频异常检测的方法，系统和处理器可读介质。 可以接收视频输入，并且可以从视频输入计算事件的两个或多个不同描述符。 描述符可以组合形成一个事件矩阵。 相对于由不同描述符表示的训练事件的完整字典，可以执行事件矩阵的稀疏重建。 然后可以执行一个步骤，以通过计算稀疏重建的异常值拒绝测量来确定事件是否是异常的。
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公开(公告)号：US20150110357A1
公开(公告)日：2015-04-23
申请号：US14326635
申请日：2014-07-09
Applicant: Xerox Corporation
Inventor： Raja Bala , Zhigang Fan , Aaron Burry , Vishal Monga , Xuan Mo
IPC: G06K9/00
CPC classification number: G06K9/6249 , G06K9/00771
Abstract: Methods and systems for detecting anomalies in transportation related video footage. In an offline training phase, receiving video footage of a traffic location can be received. Also, in an offline training phase, event encodings can be extracted from the video footage and collected or compiled into a training dictionary. One or more input video sequences captured at the traffic location or a similar traffic location can be received in an online detection phase. Then, an event encoding corresponding to the input video sequence can be extracted. The event encoding can be reconstructed with a low rank sparsity prior model applied with respect to the training dictionary. The reconstruction error between actual and reconstructed event encodings can then be computed in order to determine if an event thereof is anomalous by comparing the reconstruction error with a threshold.
Abstract translation: 检测交通相关影像异常的方法和系统。 在离线训练阶段，可以接收到接收到交通位置的录像带。 此外，在离线训练阶段，可以从视频素材中提取事件编码，并将其收集或编译成训练词典。 在在线检测阶段可以接收在业务位置或相似的业务位置处捕获的一个或多个输入视频序列。 然后，可以提取与输入视频序列相对应的事件编码。 可以使用相对于训练词典应用的低秩稀疏性先验模型来重构事件编码。 然后可以计算实际和重建事件编码之间的重建误差，以便通过将重建误差与阈值进行比较来确定其事件是否是异常的。
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6.发明申请DICTIONARY DESIGN FOR COMPUTATIONALLY EFFICIENT VIDEO ANOMALY DETECTION VIA SPARSE RECONSTRUCTION TECHNIQUES 有权通过稀疏重建技术进行计算效能视觉异常检测的词典设计公开(公告)号：US20140270353A1
公开(公告)日：2014-09-18
申请号：US13827222
申请日：2013-03-14
Applicant: XEROX CORPORATION
Inventor： Raja Bala , Zhigang Fan , Aaron Michael Burry , Jose Antonio Rodriguez-Serrano , Vishal Monga , Xuan Mo
CPC classification number: G06K9/00771 , G06K9/6249
Abstract: Methods, systems, and processor-readable media for pruning a training dictionary for use in detecting anomalous events from surveillance video. Training samples can be received, which correspond to normal events. A dictionary can then be constructed, which includes two or more classes of normal events from the training samples. Sparse codes are then generated for selected training samples with respect to the dictionary derived from the two or more classes of normal events. The size of the dictionary can then be reduced by removing redundant dictionary columns from the dictionary via analysis of the sparse codes. The dictionary is then optimized to yield a low reconstruction error and a high-interclass discriminability.
Abstract translation: 用于修剪用于检测来自监视视频的异常事件的训练词典的方法，系统和处理器可读介质。 可以收到培训样本，对应于正常事件。 然后可以构建字典，其包括来自训练样本的两个或更多类的正常事件。 然后针对从两个或多个正常事件类派生的字典为选定的训练样本生成稀疏码。 然后可以通过对稀疏代码的分析从字典中删除冗余字典列来减少字典的大小。 然后对该字典进行优化，以产生低重构误差和高阶间的可辨别性。
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