会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 3. 发明授权
    • Object tracking in video with visual constraints
    • 视频约束对象跟踪
    • US08477998B1
    • 2013-07-02
    • US13309999
    • 2011-12-02
    • Minyoung KimSanjiv KumarHenry A. Rowley
    • Minyoung KimSanjiv KumarHenry A. Rowley
    • G06K9/00
    • G06K9/00261G06K9/6214G06K9/6264G06K9/6277
    • Embodiments of the present invention relate to object tracking in video. In an embodiment, a computer-implemented method tracks an object in a frame of a video. An adaptive term value is determined based on an adaptive model and at least a portion of the frame. A pose constraint value is determined based on a pose model and at least a portion the frame. An alignment confidence score is determined based on an alignment model and at least a portion the frame. Based on the adaptive term value, the pose constraint value, and the alignment confidence score, an energy value is determined. Based on the energy value, a resultant tracking state is determined. The resultant tracking state defines a likely position of the object in the frame given the object's likely position in a set of previous frames in the video.
    • 本发明的实施例涉及视频中的对象跟踪。 在一个实施例中,计算机实现的方法跟踪视频帧中的对象。 基于自适应模型和帧的至少一部分来确定自适应项值。 基于姿态模型和帧的至少一部分来确定姿势约束值。 基于对准模型和框架的至少一部分来确定对准置信度得分。 基于自适应项值,姿态约束值和对准置信度得分,确定能量值。 基于能量值,确定合成的跟踪状态。 所得到的跟踪状态定义了给定对象在视频中的一组先前帧中的可能位置的帧中的对象的可能位置。
    • 5. 发明授权
    • Object tracking in video with visual constraints
    • 视频约束对象跟踪
    • US08085982B1
    • 2011-12-27
    • US12143590
    • 2008-06-20
    • Minyoung KimSanjiv KumarHenry A. Rowley
    • Minyoung KimSanjiv KumarHenry A. Rowley
    • G06K9/00G06K9/46G06K9/66
    • G06K9/00261G06K9/6214G06K9/6264G06K9/6277
    • Embodiments of the present invention relate to object tracking in video. In an embodiment, a computer-implemented method tracks an object in a frame of a video. An adaptive term value is determined based on an adaptive model and at least a portion of the frame. A pose constraint value is determined based on a pose model and at least a portion the frame. An alignment confidence score is determined based on an alignment model and at least a portion the frame. Based on the adaptive term value, the pose constraint value, and the alignment confidence score, an energy value is determined. Based on the energy value, a resultant tracking state is determined. The resultant tracking state defines a likely position of the object in the frame given the object's likely position in a set of previous frames in the video.
    • 本发明的实施例涉及视频中的对象跟踪。 在一个实施例中,计算机实现的方法跟踪视频帧中的对象。 基于自适应模型和帧的至少一部分来确定自适应项值。 基于姿态模型和帧的至少一部分来确定姿势约束值。 基于对准模型和框架的至少一部分来确定对准置信度得分。 基于自适应项值,姿态约束值和对准置信度得分,确定能量值。 基于能量值,确定合成的跟踪状态。 所得到的跟踪状态定义了给定对象在视频中的一组先前帧中的可能位置的帧中的对象的可能位置。