Patents by Inventor Ming-Jung Seow

Ming-Jung Seow has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 8270732
    Abstract: Techniques are disclosed for discovering object type clusters using pixel-level micro-features extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to classify objects depicted in the image data based on the pixel-level micro-features. Importantly, the discovery of the object type clusters is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects.
    Type: Grant
    Filed: August 31, 2009
    Date of Patent: September 18, 2012
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, David Friedlander, Kishor Adinath Saitwal, Ming-Jung Seow, Gang Xu
  • Patent number: 8270733
    Abstract: Techniques are disclosed for identifying anomaly object types during classification of foreground objects extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to discover object type clusters and classify objects depicted in the image data based on pixel-level micro-features that are extracted from the image data. Importantly, the discovery of the object type clusters is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects and identifying anomaly object types.
    Type: Grant
    Filed: August 31, 2009
    Date of Patent: September 18, 2012
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, David Friedlander, Rajkiran Kumar Gottumukkal, Ming-Jung Seow, Gang Xu
  • Publication number: 20120224746
    Abstract: Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A combination of a self organizing map (SOM) and an adaptive resonance theory (ART) network may be used to identify a variety of different anomalous inputs at each cluster layer. As progressively higher layers of the cortex model component represent progressively higher levels of abstraction, anomalies occurring in the higher levels of the cortex model represent observations of behavioral anomalies corresponding to progressively complex patterns of behavior.
    Type: Application
    Filed: May 15, 2012
    Publication date: September 6, 2012
    Applicant: BEHAVIORAL RECOGNITION SYSTEMS, INC.
    Inventors: Wesley Kenneth COBB, David FRIEDLANDER, Kishor Adinath SAITWAL, Ming-Jung SEOW, Gang XU
  • Patent number: 8218818
    Abstract: Techniques are disclosed for detecting foreground objects in a scene captured by a surveillance system and tracking the detected foreground objects from frame to frame in real time. A motion flow field is used to validate foreground objects(s) that are extracted from the background model of a scene. Spurious foreground objects are filtered before the foreground objects are provided to the tracking stage. The motion flow field is also used by the tracking stage to improve the performance of the tracking as needed for real time surveillance applications.
    Type: Grant
    Filed: September 1, 2009
    Date of Patent: July 10, 2012
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, Ming-Jung Seow, Tao Yang
  • Patent number: 8218819
    Abstract: Techniques are disclosed for detecting foreground objects in a scene captured by a surveillance system and tracking the detected foreground objects from frame to frame in real time. A motion flow field is used to validate foreground objects(s) that are extracted from the background model of a scene. Spurious foreground objects are filtered before the detected foreground objects are provided to the tracking stage. The motion flow field is also used by the tracking stage to improve the performance of the tracking as needed for real time surveillance applications.
    Type: Grant
    Filed: September 1, 2009
    Date of Patent: July 10, 2012
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, Ming-Jung Seow, Tao Yang
  • Publication number: 20120163670
    Abstract: Embodiments of the present invention provide a method and a system for analyzing and learning behavior based on an acquired stream of video frames. Objects depicted in the stream are determined based on an analysis of the video frames. Each object may have a corresponding search model used to track an object's motion frame-to-frame. Classes of the objects are determined and semantic representations of the objects are generated. The semantic representations are used to determine objects' behaviors and to learn about behaviors occurring in an environment depicted by the acquired video streams. This way, the system learns rapidly and in real-time normal and abnormal behaviors for any environment by analyzing movements or activities or absence of such in the environment and identifies and predicts abnormal and suspicious behavior based on what has been learned.
    Type: Application
    Filed: March 6, 2012
    Publication date: June 28, 2012
    Applicant: BEHAVIORAL RECOGNITION SYSTEMS, INC.
    Inventors: John Eric EATON, Wesley Kenneth COBB, Dennis Gene URECH, Bobby Ernest BLYTHE, David Samuel FRIEDLANDER, Rajkiran Kumar GOTTUMUKKAL, Lon William RISINGER, Kishor Adinath SAITWAL, Ming-Jung SEOW, David Marvin SOLUM, Gang XU, Tao YANG
  • Patent number: 8189905
    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.
    Type: Grant
    Filed: July 9, 2008
    Date of Patent: May 29, 2012
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: 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
  • Patent number: 8180105
    Abstract: Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A combination of a self organizing map (SOM) and an adaptive resonance theory (ART) network may be used to identify a variety of different anomalous inputs at each cluster layer. As progressively higher layers of the cortex model component represent progressively higher levels of abstraction, anomalies occurring in the higher levels of the cortex model represent observations of behavioral anomalies corresponding to progressively complex patterns of behavior.
    Type: Grant
    Filed: September 17, 2009
    Date of Patent: May 15, 2012
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, David Friedlander, Kishor Adinath Saitwal, Ming-Jung Seow, Gang Xu
  • Patent number: 8175333
    Abstract: An estimator/identifier component for a computer vision engine of a machine-learning based behavior-recognition system is disclosed. The estimator/identifier component may be configured to classify an object being one of two or more classification types, e.g., as being a vehicle or a person. Once classified, the estimator/identifier may evaluate the object to determine a set of kinematic data, static data, and a current pose of the object. The output of the estimator/identifier component may include the classifications assigned to a tracked object, as well as the derived information and object attributes.
    Type: Grant
    Filed: September 11, 2008
    Date of Patent: May 8, 2012
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: John Eric Eaton, Wesley Kenneth Cobb, Rajkiran K. Gottumukkal, Ming-Jung Seow, Tao Yang, Kishor Adinath Saitwal
  • Patent number: 8167430
    Abstract: Techniques are described for analyzing a stream of video frames to identify temporal anomalies. A video surveillance system configured to identify when agents depicted in the video stream engage in anomalous behavior, relative to the time-of-day (TOD) or day-of-week (DOW) at which the behavior occurs. A machine-learning engine may establish the normalcy of a scene by observing the scene over a specified period of time. Once the observations of the scene have matured, the actions of agents in the scene may be evaluated and classified as normal or abnormal temporal behavior, relative to the past observations.
    Type: Grant
    Filed: August 31, 2009
    Date of Patent: May 1, 2012
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, Ming-Jung Seow
  • Patent number: 8170283
    Abstract: Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A video surveillance system may be configured to observe a scene (as depicted in a sequence of video frames) and, over time, develop hierarchies of concepts including classes of objects, actions and behaviors. That is, the video surveillance system may develop models at progressively more complex levels of abstraction used to identify what events and behaviors are common and which are unusual. When the models have matured, the video surveillance system issues alerts on unusual events.
    Type: Grant
    Filed: September 17, 2009
    Date of Patent: May 1, 2012
    Assignee: Behavioral Recognition Systems Inc.
    Inventors: Wesley Kenneth Cobb, David Friedlander, Kishor Adinath Saitwal, Ming-Jung Seow, Gang Xu
  • Patent number: 8131012
    Abstract: Embodiments of the present invention provide a method and a system for analyzing and learning behavior based on an acquired stream of video frames. Objects depicted in the stream are determined based on an analysis of the video frames. Each object may have a corresponding search model used to track an object's motion frame-to-frame. Classes of the objects are determined and semantic representations of the objects are generated. The semantic representations are used to determine objects' behaviors and to learn about behaviors occurring in an environment depicted by the acquired video streams. This way, the system learns rapidly and in real-time normal and abnormal behaviors for any environment by analyzing movements or activities or absence of such in the environment and identifies and predicts abnormal and suspicious behavior based on what has been learned.
    Type: Grant
    Filed: February 8, 2008
    Date of Patent: March 6, 2012
    Assignee: BEHAVIORAL RECOGNITION SYSTEMS, Inc.
    Inventors: John Eric Eaton, Wesley Kenneth Cobb, Dennis Gene Urech, Bobby Ernest Blythe, David Samuel Friedlander, Rajkiran Kumar Gottumukkal, Lon William Risinger, Kishor Adinath Saitwal, Ming-Jung Seow, David Marvin Solum, Gang Xu, Tao Yang
  • Patent number: 8126833
    Abstract: Techniques are described for detecting anomalous events using a long-term memory in a video analysis system. The long-term memory may be used to store and retrieve information learned while a video analysis system observes a stream of video frames depicting a given scene. Further, the long-term memory may be configured to detect the occurrence of anomalous events, relative to observations of other events that have occurred in the scene over time. A distance measure may used to determine a distance between an active percept (encoding an observed event depicted in the stream of video frames) and a retrieved percept (encoding a memory of previously observed events in the long-term memory). If the distance exceeds a specified threshold, the long-term memory may publish the occurrence of an anomalous event for review by users of the system.
    Type: Grant
    Filed: December 16, 2008
    Date of Patent: February 28, 2012
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, Ming-Jung Seow, Gang Xu
  • Patent number: 8121968
    Abstract: A long-term memory used to store and retrieve information learned while a video analysis system observes a stream of video frames is disclosed. The long-term memory provides a memory with a capacity that grows in size gracefully, as events are observed over time. Additionally, the long-term memory may encode events, represented by sub-graphs of a neural network. Further, rather than predefining a number of patterns recognized and manipulated by the long-term memory, embodiments of the invention provide a long-term memory where the size of a feature dimension (used to determine the similarity between different observed events) may grow dynamically as necessary, depending on the actual events observed in a sequence of video frames.
    Type: Grant
    Filed: September 11, 2008
    Date of Patent: February 21, 2012
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: John Eric Eaton, Wesley Kenneth Cobb, Ming-Jung Seow, David Samuel Friedlander, Gang Xu
  • Patent number: 8094943
    Abstract: Embodiments of the present invention provide a method and a module for identifying a background of a scene depicted in an acquired stream of video frames that may be used by a video-analysis system. For each pixel or block of pixels in an acquired video frame a comparison measure is determined. The comparison measure depends on difference of color values exhibited in the acquired video frame and in a background image respectively by the pixel or block of pixels and a corresponding pixel and block of pixels in the background image. To determine the comparison measure, the resulting difference is considered in relation to a range of possible color values. If the comparison measure is above a dynamically adjusted threshold, the pixel or the block of pixels is classified as a part of the background of the scene.
    Type: Grant
    Filed: May 29, 2008
    Date of Patent: January 10, 2012
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: John Eric Eaton, Wesley Kenneth Cobb, Bobby Ernest Blythe, Kishor Adinath Saitwal, Tao Yang, Ming-Jung Seow
  • Publication number: 20110064267
    Abstract: Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A combination of a self organizing map (SOM) and an adaptive resonance theory (ART) network may be used to identify a variety of different anomalous inputs at each cluster layer. As progressively higher layers of the cortex model component represent progressively higher levels of abstraction, anomalies occurring in the higher levels of the cortex model represent observations of behavioral anomalies corresponding to progressively complex patterns of behavior.
    Type: Application
    Filed: September 17, 2009
    Publication date: March 17, 2011
    Inventors: WESLEY KENNETH COBB, David Friedlander, Kishor Adinath Saitwal, Ming-Jung Seow, Gang Xu
  • Publication number: 20110064268
    Abstract: Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A video surveillance system may be configured to observe a scene (as depicted in a sequence of video frames) and, over time, develop hierarchies of concepts including classes of objects, actions and behaviors. That is, the video surveillance system may develop models at progressively more complex levels of abstraction used to identify what events and behaviors are common and which are unusual. When the models have matured, the video surveillance system issues alerts on unusual events.
    Type: Application
    Filed: September 17, 2009
    Publication date: March 17, 2011
    Inventors: WESLEY KENNETH COBB, David Friedlander, Kishor Adinath Saitwal, Ming-Jung Seow, Gang Xu
  • Publication number: 20110050896
    Abstract: Techniques are disclosed for visually conveying a percept. The percept may represent information learned by a video surveillance system. A request may be received to view a percept for a specified scene. The percept may have been derived from data streams generated from a sequence of video frames depicting the specified scene captured by a video camera. A visual representation of the percept may be generated. A user interface may be configured to display the visual representation of the percept and to allow a user to view and/or modify metadata attributes with the percept. For example, the user may label a percept and set events matching the percept to always (or never) result in alert being generated for users of the video surveillance system.
    Type: Application
    Filed: August 31, 2009
    Publication date: March 3, 2011
    Inventors: WESLEY KENNETH COBB, BOBBY ERNEST BLYTHE, RAJKIRAN KUMAR GOTTUMUKKAL, MING-JUNG SEOW
  • Publication number: 20110051992
    Abstract: Techniques are described for analyzing a stream of video frames to identify temporal anomalies. A video surveillance system configured to identify when agents depicted in the video stream engage in anomalous behavior, relative to the time-of-day (TOD) or day-of-week (DOW) at which the behavior occurs. A machine-learning engine may establish the normalcy of a scene by observing the scene over a specified period of time. Once the observations of the scene have matured, the actions of agents in the scene may be evaluated and classified as normal or abnormal temporal behavior, relative to the past observations.
    Type: Application
    Filed: August 31, 2009
    Publication date: March 3, 2011
    Inventors: WESLEY KENNETH COBB, Ming-Jung Seow
  • Publication number: 20110050897
    Abstract: Techniques are disclosed for visually conveying classifications derived from pixel-level micro-features extracted from image data. The image data may include an input stream of video frames depicting one or more foreground objects. The classifications represent information learned by a video surveillance system. A request may be received to view a classification. A visual representation of the classification may be generated. A user interface may be configured to display the visual representation of the classification and to allow a user to view and/or modify properties associated with the classification.
    Type: Application
    Filed: August 31, 2009
    Publication date: March 3, 2011
    Inventors: Wesley Kenneth Cobb, Bobby Ernest Blythe, David Samuel Friedlander, Rajkiran Kumar Gottumukkal, Kishor Adinath Saitwal, Ming-Jung Seow, Gang Xu