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    • 42. 发明申请
    • Automated searching for probable matches in a video surveillance system
    • 在视频监控系统中自动搜索可能的匹配
    • US20070025593A1
    • 2007-02-01
    • US11398159
    • 2006-04-04
    • Gordon HauptStephen FleischerRobert ValloneStephen RussellTimothy Frederick
    • Gordon HauptStephen FleischerRobert ValloneStephen RussellTimothy Frederick
    • G06K9/00
    • G06K9/00771
    • A technique for searching for probable matches in a video surveillance system is disclosed. A new event, such as a face captured in an image set, is matched against other events in a database of events. A similarity score is generated based on the difference between the new event and other events in the database. The similarity score may be weighted by information external to the image sets. Because of limited system resources, an association between a new event and every other event in the system may not be kept. Thus, when searching for probable matches of a particular event, some events that are related to the particular event may not be initially selected. Such events may be associated with an event in a first set of events that are associated with the particular event. Therefore, a second set of events is selected that are associated with the first set of events.
    • 公开了一种在视频监控系统中搜索可能匹配的技术。 诸如在图像集中捕获的脸部的新事件与事件数据库中的其他事件相匹配。 基于新事件和数据库中的其他事件之间的差异生成相似度分数。 可以通过图像集外部的信息来加权相似性得分。 由于系统资源有限,系统中的新事件和其他事件之间的关联可能无法保留。 因此,当搜索特定事件的可能匹配时,可能不会最初选择与特定事件相关的一些事件。 这种事件可以与与特定事件相关联的第一组事件中的事件相关联。 因此,选择与第一组事件相关联的第二组事件。
    • 47. 发明申请
    • Correlation processing among multiple analyzers of video streams at stages of a pipeline architecture
    • 在管道架构的阶段,视频流的多个分析器之间的相关处理
    • US20050259846A1
    • 2005-11-24
    • US10965675
    • 2004-10-13
    • J. FreemanRobert ValloneStephen RussellStephen FleischerGordon Haupt
    • J. FreemanRobert ValloneStephen RussellStephen FleischerGordon Haupt
    • G06K9/00
    • G06K9/00973
    • A pipeline architecture for analyzing multiple streams of video is embodied, in part, in a layer of application program interfaces (APIs) to each stage of processing. Buffer queuing is used between some stages, which helps moderate the load on the CPU(s). Through the layer of APIs, innumerable video analysis applications can access and analyze video data flowing through the pipeline, and can annotate portions of the video data (e.g., frames and groups of frames), based on the analyses performed, with information that describes the frame or group. These annotated frames and groups flow through the pipeline to subsequent stages of processing, at which increasingly complex analyses can be performed. At each stage, portions of the video data that are of little or no interest are removed from the video data. Ultimately, “events” are constructed and stored in a database, from which cross-event and historical analyses may be performed and associations with, and among, events may be made.
    • 用于分析多个视频流的流水线架构部分地体现在每个处理阶段的应用程序接口(API)层中。 在一些阶段之间使用缓冲区排队,这有助于缓和CPU上的负载。 通过API层,无数视频分析应用程序可以访问和分析流经流水线的视频数据,并且可以基于所执行的分析来注释视频数据的一部分(例如,帧和帧组),其中描述 框架或组。 这些注释的帧和组流经管线到处理的后续阶段,在该阶段可以执行越来越复杂的分析。 在每个阶段,从视频数据中删除少量或不感兴趣的视频数据的部分。 最终,“事件”被构建并存储在数据库中,可以从该数据库中进行交叉事件和历史分析,并且可以进行与事件的关联以及事件之间的关联。