公开(公告)号：WO2017197095A1

公开(公告)日：2017-11-16

申请号：PCT/US2017/032125

申请日：2017-05-11

Applicant: ARRIS ENTERPRISES LLC

Inventor： LI, Renxiang ,  EMEOTT, Stephen P. ,  ISHTIAQ, Faisal

IPC: G06K9/00 ,  H04N21/81 ,  H04N21/234

CPC classification number: H04N21/23424 ,  G06K9/00758 ,  H04N21/232 ,  H04N21/23418 ,  H04N21/24 ,  H04N21/812 ,  H04N21/8455

Abstract: Particular embodiments can refine a seed sentinel frame signature for a seed sentinel frame. The seed sentinel frame may be predictable or partially predictable content that demarks a beginning and/or end of certain content in a video program. The seed sentinel frame may be first used to detect other sentinel frames in the video program. However, other sentinel frames throughout the video program, or in other video programs, may be slightly different from the given sentinel frame due to different reasons. The seed sentinel frame signature may not detect the sentinel frames of a video program with a desired accuracy. Accordingly, particular embodiments may refine the sentinel frame signature to a synthetic sentinel frame signature. The synthetic sentinel frame signature may then be used to analyze the current video program or other video programs. The synthetic sentinel frame signature may more accurately detect the sentinel frames within the video program.

Abstract translation: 特定实施例可以为种子哨兵帧改进种子哨兵帧签名。 种子哨兵帧可以是对视频节目中特定内容的开始和/或结束进行分类的可预测或部分可预测的内容。 种子哨兵帧可以首先用于检测视频节目中的其他哨兵帧。 但是，由于不同的原因，在整个视频节目或其他视频节目中的其他哨兵帧可能与给定的哨兵帧稍有不同。 种子哨点帧签名可能不会以期望的准确度检测视频节目的哨兵帧。 相应地，特定实施例可以将哨兵帧签名改进为合成哨兵帧签名。 然后可以使用合成哨点帧签名来分析当前视频节目或其他视频节目。 合成的哨兵帧签名可以更准确地检测视频节目内的哨兵帧。

公开(公告)号：WO2017197090A1

公开(公告)日：2017-11-16

申请号：PCT/US2017/032117

申请日：2017-05-11

Applicant: ARRIS ENTERPRISES LLC

Inventor： EMEOTT, Stephen P. ,  LI, Renxiang

IPC: G06K9/00

CPC classification number: G06K9/00758 ,  G06K9/00744 ,  G06K9/00765 ,  G06K9/6201

Abstract: Particular embodiments extract features for frames from a video. Then, a sequence of frames is identified based on a pattern analysis of frames based on the features. This pattern may be used to select sentinel features from the sequence of frames. For example, a pattern may include a first sentinel sequence followed by a transitional frame and then a second sentinel sequence. The transitional frame may be a black frame that is used to identify back-to-back sentinel sequences. Then, a sentinel frame that demarks a transition from a first content type to a second content type is identified. For example, the frames on either side of the black frame may be very similar and be identified as sentinel frames that mark a transition from program content to advertisement content. The above process may allow automated detection of sentinel frames that can run without user supervision.

Abstract translation: 特定实施例提取来自视频的帧的特征。 然后，基于基于特征的帧的模式分析来识别帧的序列。 这种模式可用于从帧序列中选择哨兵特征。 例如，模式可以包括第一前哨序列，后面是过渡帧，然后是第二前哨序列。 过渡帧可以是用于识别背靠背哨兵序列的黑帧。 然后，识别从第一内容类型到第二内容类型的转换的标记帧。 例如，黑色框两侧的帧可能非常相似，并且被标识为标记从节目内容到广告内容的转变的哨兵帧。 上述过程可以允许自动检测可在没有用户监督的情况下运行的哨兵帧。