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    • 1. 发明申请
    • SCENE ACTIVITY ANALYSIS USING STATISTICAL AND SEMANTIC FEATURE LEARNT FROM OBJECT TRAJECTORY DATA
    • 使用从对象轨迹数据中获得的统计和语义特征的场景活动分析
    • WO2012092148A2
    • 2012-07-05
    • PCT/US2011066962
    • 2011-12-22
    • PELCO INCMILLAR GREGAGHDASI FARZINZHU HONGWEI
    • MILLAR GREGAGHDASI FARZINZHU HONGWEI
    • G06T7/20H04N5/91H04N7/18
    • G06K9/00785
    • Trajectory information of objects appearing in a scene can be used to cluster trajectories into groups of trajectories according to each trajectory's relative distance between each other for scene activity analysis. By doing so, a database of trajectory data can be maintained that includes the trajectories to be clustered into trajectory groups. This database can be used to train a clustering system, and with extracted statistical features of resultant trajectory groups a new trajectory can be analyzed to determine whether the new trajectory is normal or abnormal. Embodiments described herein, can be used to determine whether a video scene is normal or abnormal. In the event that the new trajectory is identified as normal the new trajectory can be annotated with the extracted semantic data. In the event that the new trajectory is determined to be abnormal a user can be notified that an abnormal behavior has occurred.
    • 出现在场景中的物体的轨迹信息可用于根据每个轨迹的相对距离将轨迹分组到轨迹组,以进行场景活动分析。 通过这样做,可以保持轨迹数据的数据库,其包括要聚集成轨迹组的轨迹。 该数据库可用于训练聚类系统,并且通过提取的结果轨迹组的统计特征,可以分析新的轨迹,以确定新轨迹是正常还是异常。 本文描述的实施例可以用于确定视频场景是正常还是异常。 在新轨迹被识别为正常的情况下,可以用所提取的语义数据来注释新的轨迹。 在新轨迹被确定为异常的情况下,可以通知用户发生异常行为。
    • 2. 发明申请
    • CLUSTERING-BASED OBJECT CLASSIFICATION
    • 基于聚类的对象分类
    • WO2013101460A3
    • 2013-10-03
    • PCT/US2012069148
    • 2012-12-12
    • PELCO INCZHU HONGWEIAGHDASI FARZINMILLAR GREG
    • ZHU HONGWEIAGHDASI FARZINMILLAR GREG
    • G06K9/68
    • G06K9/68
    • An example of a method for identifying objects in video content according to the disclosure includes receiving video content of a scene captured by a video camera, detecting an object in the video content, identifying a track that the object follows over a series of frames of the video content, extracting object features for the object from the video content, and classifying the object based on the object features. Classifying the object further comprises: determining a track-level classification for the object using spatially invariant object features, determining a global-clustering classification for the object using spatially variant features, and determining an object type for the object based on the track-level classification and the global-clustering classification for the object.
    • 根据本公开的用于识别视频内容中的对象的方法的示例包括:接收由摄像机捕获的场景的视频内容,检测视频内容中的对象,识别该对象在一系列帧上跟随的轨迹 视频内容,从视频内容中提取对象的对象特征,以及基于对象特征对对象进行分类。 分类对象还包括:使用空间不变对象特征来确定对象的轨道级分类,使用空间变异特征确定对象的全局聚类分类,以及基于轨道级分类来确定对象的对象类型 和对象的全局聚类分类。