公开(公告)号：US20160163065A1

公开(公告)日：2016-06-09

申请号：US15019759

申请日：2016-02-09

Applicant: 9051147 CANADA INC.

Inventor： Wesley Kenneth COBB ,  Ming-Jung SEOW ,  Tao YANG

IPC: G06T7/20 ,  G06K9/66 ,  G06K9/00

CPC classification number: G06T7/20 ,  G06K9/00335 ,  G06K9/00718 ,  G06K9/00771 ,  G06K9/6218 ,  G06K9/6222 ,  G06K9/66 ,  G06K2009/00738 ,  G06T7/254 ,  G06T2207/10016 ,  G06T2207/20084 ,  G06T2207/30196 ,  G06T2207/30232

Abstract: Systems and methods for viewing a scene depicted in a sequence of video frames and identifying and tracking objects between separate frames of the sequence. Each tracked object is classified based on known categories and a stream of context events associated with the object is generated. A sequence of primitive events based on the stream of context events is generated and stored together, along with detailed data and generalized data related to an event. All of the data is then evaluated to learn patterns of behavior that occur within the scene.

Abstract translation: 用于观看视频帧序列中描绘的场景的系统和方法，并且在序列的不同帧之间识别和跟踪对象。 基于已知类别对每个被跟踪对象进行分类，并且生成与对象相关联的上下文事件流。 基于上下文事件流的原始事件序列被生成并存储在一起，以及与事件相关的详细数据和通用数据。 然后评估所有数据以了解场景内出现的行为模式。

公开(公告)号：US20160125233A1

公开(公告)日：2016-05-05

申请号：US14992973

申请日：2016-01-11

Applicant: 9051147 CANADA INC.

Inventor： John Eric EATON ,  Wesley Kenneth COBB ,  Dennis G. URECH ,  David S. FRIEDLANDER ,  Gang XU ,  Ming-Jung SEOW ,  Lon W. RISINGER ,  David M. SOLUM ,  Tao YANG ,  Rajkiran K. GOTTUMUKKAL ,  Kishor Adinath SAITWAL

IPC: G06K9/00 ,  G06N99/00 ,  G06K9/66

CPC classification number: G06K9/00718 ,  G06F17/30598 ,  G06K9/00335 ,  G06K9/00751 ,  G06K9/00771 ,  G06K9/6215 ,  G06K9/66 ,  G06K2009/00738 ,  G06N99/005

Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.