Patents by Inventor Kishor Adinath Saitwal

Kishor Adinath Saitwal 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: 8340352
    Abstract: A sequence layer in a machine-learning engine configured to learn from the observations of a computer vision engine. In one embodiment, the machine-learning engine uses the voting experts to segment adaptive resonance theory (ART) network label sequences for different objects observed in a scene. The sequence layer may be configured to observe the ART label sequences and incrementally build, update, and trim, and reorganize an ngram trie for those label sequences. The sequence layer computes the entropies for the nodes in the ngram trie and determines a sliding window length and vote count parameters. Once determined, the sequence layer may segment newly observed sequences to estimate the primitive events observed in the scene as well as issue alerts for inter-sequence and intra-sequence anomalies.
    Type: Grant
    Filed: August 18, 2009
    Date of Patent: December 25, 2012
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, David Samuel Friedlander, Kishor Adinath Saitwal
  • Patent number: 8300924
    Abstract: A tracker component for a computer vision engine of a machine-learning based behavior-recognition system is disclosed. The behavior-recognition system may be configured to learn, identify, and recognize patterns of behavior by observing a video stream (i.e., a sequence of individual video frames). The tracker component may be configured to track objects depicted in the sequence of video frames and to generate, search, match, and update computational models of such objects.
    Type: Grant
    Filed: September 11, 2008
    Date of Patent: October 30, 2012
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: John Eric Eaton, Wesley Kenneth Cobb, Rajkiran K. Gottumukkal, Kishor Adinath Saitwal, Ming-Jung Seow, Tao Yang, Bobby Ernest Blythe
  • Patent number: 8295591
    Abstract: A sequence layer in a machine-learning engine configured to learn from the observations of a computer vision engine. In one embodiment, the machine-learning engine uses the voting experts to segment adaptive resonance theory (ART) network label sequences for different objects observed in a scene. The sequence layer may be configured to observe the ART label sequences and incrementally build, update, and trim, and reorganize an ngram trie for those label sequences. The sequence layer computes the entropies for the nodes in the ngram trie and determines a sliding window length and vote count parameters. Once determined, the sequence layer may segment newly observed sequences to estimate the primitive events observed in the scene as well as issue alerts for inter-sequence and intra-sequence anomalies.
    Type: Grant
    Filed: August 18, 2009
    Date of Patent: October 23, 2012
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, Bobby Ernest Blythe, David Samuel Friedlander, Kishor Adinath Saitwal, Gang Xu
  • Publication number: 20120257831
    Abstract: Embodiments of the present invention provide a method and a system for mapping a scene depicted in an acquired stream of video frames that may be used by a machine-learning behavior-recognition system. A background image of the scene is segmented into plurality of regions representing various objects of the background image. Statistically similar regions may be merged and associated. The regions are analyzed to determine their z-depth order in relation to a video capturing device providing the stream of the video frames and other regions, using occlusions between the regions and data about foreground objects in the scene. An annotated map describing the identified regions and their properties is created and updated.
    Type: Application
    Filed: June 12, 2012
    Publication date: October 11, 2012
    Applicant: BEHAVIORAL RECOGNITION SYSTEMS, INC.
    Inventors: John Eric Eaton, Wesley Kenneth Cobb, Bobby Ernest Blythe, Rajkiran Kumar Gottumukkal, Kishor Adinath Saitwal
  • Patent number: 8285046
    Abstract: Techniques are disclosed for a computer vision engine to update both a background model and thresholds used to classify pixels as depicting scene foreground or background in response to detecting that a sudden illumination changes has occurred in a sequence of video frames. The threshold values may be used to specify how much pixel a given pixel may differ from corresponding values in the background model before being classified as depicting foreground. When a sudden illumination change is detected, the values for pixels affected by sudden illumination change may be used to update the value in the background image to reflect the value for that pixel following the sudden illumination change as well as update the threshold for classifying that pixel as depicting foreground/background in subsequent frames of video.
    Type: Grant
    Filed: February 18, 2009
    Date of Patent: October 9, 2012
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, Kishor Adinath Saitwal, Bobby Ernest Blythe, Tao Yang
  • 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
  • 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
  • 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: 8200011
    Abstract: Embodiments of the present invention provide a method and a system for mapping a scene depicted in an acquired stream of video frames that may be used by a machine-learning behavior-recognition system. A background image of the scene is segmented into plurality of regions representing various objects of the background image. Statistically similar regions may be merged and associated. The regions are analyzed to determine their z-depth order in relation to a video capturing device providing the stream of the video frames and other regions, using occlusions between the regions and data about foreground objects in the scene. An annotated map describing the identified regions and their properties is created and updated.
    Type: Grant
    Filed: April 30, 2008
    Date of Patent: June 12, 2012
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: John Eric Eaton, Wesley Kenneth Cobb, Bobby Ernest Blythe, Rajkiran Kumar Gottumukkal, Kishor Adinath Saitwal
  • 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: 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: 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
  • Patent number: 8064695
    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: November 22, 2011
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: John Eric Eaton, Wesley Kenneth Cobb, Kishor Adinath Saitwal, Bobby Ernest Blythe
  • Patent number: 8041116
    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: October 18, 2011
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: John Eric Eaton, Wesley Kenneth Cobb, Kishor Adinath Saitwal, Bobby Ernest Blythe
  • 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: 20110052067
    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: Application
    Filed: August 31, 2009
    Publication date: March 3, 2011
    Inventors: WESLEY KENNETH COBB, David Friedlander, Kishor Adinath Saitwal, Ming-Jung Seow, Gang Xu