Patents Assigned to Behavioral Recognition Systems, Inc.
  • Patent number: 9507768
    Abstract: Embodiments presented herein describe a method for processing streams of data of one or more networked computer systems. According to one embodiment of the present disclosure, an ordered stream of normalized vectors corresponding to information security data obtained from one or more sensors monitoring a computer network is received. A neuro-linguistic model of the information security data is generated by clustering the ordered stream of vectors and assigning a letter to each cluster, outputting an ordered sequence of letters based on a mapping of the ordered stream of normalized vectors to the clusters, building a dictionary of words from of the ordered output of letters, outputting an ordered stream of words based on the ordered output of letters, and generating a plurality of phrases based on the ordered output of words.
    Type: Grant
    Filed: August 11, 2014
    Date of Patent: November 29, 2016
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, Ming-Jung Seow, Curtis Edward Cole, Jr., Cody Shay Falcon, Benjamin A. Konosky, Charles Richard Morgan, Aaron Poffenberger, Thong Toan Nguyen
  • Patent number: 9471844
    Abstract: Techniques are disclosed for creating a background model of a scene using both a pixel based approach and a context based approach. The combined approach provides an effective technique for segmenting scene foreground from background in frames of a video stream. Further, this approach can scale to process large numbers of camera feeds simultaneously, e.g., using parallel processing architectures, while still generating an accurate background model. Further, using both a pixel based approach and context based approach ensures that the video analytics system can effectively and efficiently respond to changes in a scene, without overly increasing computational complexity. In addition, techniques are disclosed for updating the background model, from frame-to-frame, by absorbing foreground pixels into the background model via an absorption window, and dynamically updating background/foreground thresholds.
    Type: Grant
    Filed: October 29, 2014
    Date of Patent: October 18, 2016
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Kishor Adinath Saitwal, Lon Risinger, Wesley Kenneth Cobb
  • Patent number: 9412027
    Abstract: A behavioral recognition system may include both a computer vision engine and a machine learning engine configured to observe and learn patterns of behavior in video data. Certain embodiments may be configured to detect and evaluate the presence of sea-surface oil on the water surrounding an offshore oil platform. The computer vision engine may be configured to segment image data into detected patches or blobs of surface oil (foreground) present in the field of view of an infrared camera (or cameras). A machine learning engine may evaluate the detected patches of surface oil to learn to distinguish between sea-surface oil incident to the operation of an offshore platform and the appearance of surface oil that should be investigated by platform personnel.
    Type: Grant
    Filed: August 11, 2015
    Date of Patent: August 9, 2016
    Assignee: Behavioral Recognition Systems, Inc.
    Inventor: Wesley Kenneth Cobb
  • Patent number: 9373055
    Abstract: Techniques are disclosed for detecting sudden illumination changes using radiance consistency within a spatial neighborhood. A background/foreground (BG/FG) component of a behavior recognition system may be configured to generate a background image depicting a scene background. Further, the (BG/FG) component may periodically evaluate a current video frame to determine whether a sudden illumination change has occurred. A sudden illumination change occurs when scene lighting changes dramatically from one frame to the next (or over a small number of frames).
    Type: Grant
    Filed: December 16, 2008
    Date of Patent: June 21, 2016
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, Kishor Adinath Saitwal
  • Patent number: 9349054
    Abstract: Techniques are disclosed for creating a background model of a scene using both a pixel based approach and a context based approach. The combined approach provides an effective technique for segmenting scene foreground from background in frames of a video stream. Further, this approach can scale to process large numbers of camera feeds simultaneously, e.g., using parallel processing architectures, while still generating an accurate background model. Further, using both a pixel based approach and context based approach ensures that the video analytics system can effectively and efficiently respond to changes in a scene, without overly increasing computational complexity. In addition, techniques are disclosed for updating the background model, from frame-to-frame, by absorbing foreground pixels into the background model via an absorption window, and dynamically updating background/foreground thresholds.
    Type: Grant
    Filed: October 29, 2014
    Date of Patent: May 24, 2016
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Kishor Adinath Saitwal, Lon Risinger, Wesley Kenneth Cobb
  • Patent number: 9317908
    Abstract: Techniques are disclosed for analyzing a scene depicted in an input stream of video frames captured by a video camera. Bounding boxes are determined for a set foreground patches identified in a video frame. For each bounding box, the techniques include determining textures for first areas, each including a foreground pixel and surrounding pixels, and determining textures for second areas including pixels of the background model image corresponding to the pixels of the foreground areas. Further, for each foreground pixel in the bounding box area, a correlation score is determined based on the texture of the corresponding first area and second area. Pixels whose correlation scores exceed a threshold are removed from the foreground patch. The size of the bounding box may also be reduced to fit the modified foreground patch.
    Type: Grant
    Filed: June 28, 2013
    Date of Patent: April 19, 2016
    Assignee: Behavioral Recognition System, Inc.
    Inventors: Ming-Jung Seow, Tao Yang, Wesley Kenneth Cobb
  • Patent number: 8923609
    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: April 2, 2013
    Date of Patent: December 30, 2014
    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: 8797405
    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: Grant
    Filed: August 31, 2009
    Date of Patent: August 5, 2014
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, Bobby Ernest Blythe, David Samuel Friedlander, Rajkiran Kumar Gottumukkal, Kishor Adinath Saitwal, Ming-Jung Seow, Gang Xu
  • Patent number: 8786702
    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: Grant
    Filed: August 31, 2009
    Date of Patent: July 22, 2014
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, Bobby Ernest Blythe, Rajkiran Kumar Gottumukkal, Ming-Jung Seow
  • Publication number: 20140132786
    Abstract: A behavioral recognition system may include both a computer vision engine and a machine learning engine configured to observe and learn patterns of behavior in video data. Certain embodiments may provide image stabilization of a video stream obtained from a camera. An image stabilization module in the behavioral recognition system obtains a reference image from the video stream. The image stabilization module identifies alignment regions within the reference image based on the regions of the image that are dense with features. Upon determining that the tracked features of a current image is out of alignment with the reference image, the image stabilization module uses the most feature dense alignment region to estimate an affine transformation matrix to apply to the entire current image to warp the image into proper alignment.
    Type: Application
    Filed: November 11, 2013
    Publication date: May 15, 2014
    Applicant: Behavioral Recognition Systems, Inc.
    Inventors: Kishor Adinath SAITWAL, Wesley Kenneth COBB, Tao YANG
  • Patent number: 8705861
    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: June 12, 2012
    Date of Patent: April 22, 2014
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: John Eric Eaton, Wesley Kenneth Cobb, Bobby Ernest Blythe, Rajkiran Kumar Gottumukkal, Kishor Adinath Saitwal
  • Publication number: 20140050355
    Abstract: A behavioral recognition system may include both a computer vision engine and a machine learning engine configured to observe and learn patterns of behavior in video data. Certain embodiments may be configured to detect and evaluate the presence of sea-surface oil on the water surrounding an offshore oil platform. The computer vision engine may be configured to segment image data into detected patches or blobs of surface oil (foreground) present in the field of view of an infrared camera (or cameras). A machine learning engine may evaluate the detected patches of surface oil to learn to distinguish between sea-surface oil incident to the operation of an offshore platform and the appearance of surface oil that should be investigated by platform personnel.
    Type: Application
    Filed: August 20, 2013
    Publication date: February 20, 2014
    Applicant: Behavioral Recognition Systems, Inc.
    Inventor: Wesley Kenneth COBB
  • Patent number: 8625884
    Abstract: Techniques are disclosed for visually conveying an event map. The event map may represent information learned by a surveillance system. A request may be received to view the event map for a specified scene. The event map may be generated, including a background model of the specified scene and at least one cluster providing a statistical distribution of an event in the specified scene. Each statistical distribution may be derived from data streams generated from a sequence of video frames depicting the specified scene captured by a video camera. Each event may be observed to occur at a location in the specified scene corresponding to a location of the respective cluster in the event map. The event map may be configured to allow a user to view and/or modify properties associated with each cluster. For example, the user may label a cluster and set events matching the cluster to always (or never) generate an alert.
    Type: Grant
    Filed: August 18, 2009
    Date of Patent: January 7, 2014
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, Bobby Ernest Blythe, Rajkiran Kumar Gottumukkal, Ming-Jung Seow
  • Patent number: 8620028
    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: March 6, 2012
    Date of Patent: December 31, 2013
    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: 8548198
    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: September 18, 2012
    Date of Patent: October 1, 2013
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, David Friedlander, Rajkiran Kumar Gottumukkal, Ming-Jung Seow, Gang Xu
  • Patent number: 8494222
    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: May 15, 2012
    Date of Patent: July 23, 2013
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, David Friedlander, Kishor Adinath Saitwal, Ming-Jung Seow, Gang Xu
  • Patent number: 8493409
    Abstract: Techniques are disclosed for visually conveying a sequence storing an ordered string of symbols generated from kinematic data derived from analyzing an input stream of video frames depicting one or more foreground objects. The sequence may represent information learned by a video surveillance system. A request may be received to view the sequence or a segment partitioned form the sequence. A visual representation of the segment may be generated and superimposed over a background image associated with the scene. A user interface may be configured to display the visual representation of the sequence or segment and to allow a user to view and/or modify properties associated with the sequence or segment.
    Type: Grant
    Filed: August 18, 2009
    Date of Patent: July 23, 2013
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, Bobby Ernest Blythe, David Samuel Friedlander, Rajkiran Kumar Gottumukkal, Kishor Adinath Saitwal
  • Patent number: 8416296
    Abstract: Techniques are disclosed for detecting the occurrence of unusual events in a sequence of video frames Importantly, what is determined as unusual need not be defined in advance, but can be determined over time by observing a stream of primitive events and a stream of context events. A mapper component may be configured to parse the event streams and supply input data sets to multiple adaptive resonance theory (ART) networks. Each individual ART network may generate clusters from the set of inputs data supplied to that ART network. Each cluster represents an observed statistical distribution of a particular thing or event being observed that ART network.
    Type: Grant
    Filed: April 14, 2009
    Date of Patent: April 9, 2013
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, Ming-Jung Seow
  • Patent number: 8411935
    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: April 2, 2013
    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: 8379085
    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: February 19, 2013
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, David Samuel Friedlander, Kishor Adinath Saitwal