Patent application number | Description | Published |
20090016599 | SEMANTIC REPRESENTATION MODULE OF A MACHINE-LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM - 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. | 01-15-2009 |
20090016600 | COGNITIVE MODEL FOR A MACHINE-LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM - 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. | 01-15-2009 |
20090087085 | TRACKER COMPONENT FOR BEHAVIORAL RECOGNITION SYSTEM - 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. | 04-02-2009 |
20100208986 | ADAPTIVE UPDATE OF BACKGROUND PIXEL THRESHOLDS USING SUDDEN ILLUMINATION CHANGE DETECTION - 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. | 08-19-2010 |
20110043625 | SCENE PRESET IDENTIFICATION USING QUADTREE DECOMPOSITION ANALYSIS - Techniques are disclosed for matching a current background scene of an image received by a surveillance system with a gallery of scene presets that each represent a previously captured background scene. A quadtree decomposition analysis is used to improve the robustness of the matching operation when the scene lighting changes (including portions containing over-saturation/under-saturation) or a portion of the content changes. The current background scene is processed to generate a quadtree decomposition including a plurality of window portions. Each of the window portions is processed to generate a plurality of phase spectra. The phase spectra are then projected onto a corresponding plurality of scene preset image matrices of one or more scene preset. When a match between the current background scene and one of the scene presets is identified, the matched scene preset is updated. Otherwise a new scene preset is created based on the current background scene. | 02-24-2011 |
20110043689 | FIELD-OF-VIEW CHANGE DETECTION - Techniques are disclosed for detecting a field-of-view change for a video feed. These techniques differentiate between a new or changed scene and a temporary variation in the scene to accurately detect field-of-view changes for the video feed. A field-of-view change is detected when the position of a camera providing the video feed changes, the video feed is switched to a different camera, the video feed is disconnected, or the camera providing the video feed is obscured. A false-positive field-of-view change is not detected when the scene changes due to a sudden variation in illumination, obstruction of a portion of the camera providing the video feed, blurred images due to an out-of-focus camera, or a transition between bright and dark light when the video feed transitions between color and near infrared capture modes. | 02-24-2011 |
20110044537 | BACKGROUND MODEL FOR COMPLEX AND DYNAMIC SCENES - Techniques are disclosed for learning and modeling a background for a complex and/or dynamic scene over a period of observations without supervision. A background/foreground component of a computer vision engine may be configured to model a scene using an array of ART networks. The ART networks learn the regularity and periodicity of the scene by observing the scene over a period of time. Thus, the ART networks allow the computer vision engine to model complex and dynamic scene backgrounds in video. | 02-24-2011 |
20110052002 | FOREGROUND OBJECT TRACKING - 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. | 03-03-2011 |
20110052003 | FOREGROUND OBJECT DETECTION IN A VIDEO SURVEILLANCE SYSTEM - 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. | 03-03-2011 |
20120275649 | FOREGROUND OBJECT TRACKING - 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. | 11-01-2012 |
20130136353 | BACKGROUND MODEL FOR COMPLEX AND DYNAMIC SCENES - Techniques are disclosed for learning and modeling a background for a complex and/or dynamic scene over a period of observations without supervision. A background/foreground component of a computer vision engine may be configured to model a scene using an array of ART networks. The ART networks learn the regularity and periodicity of the scene by observing the scene over a period of time. Thus, the ART networks allow the computer vision engine to model complex and dynamic scene backgrounds in video. | 05-30-2013 |
20140003713 | AUTOMATIC GAIN CONTROL FILTER IN A VIDEO ANALYSIS SYSTEM | 01-02-2014 |
20140003720 | ADAPTIVE ILLUMINANCE FILTER IN A VIDEO ANALYSIS SYSTEM | 01-02-2014 |
20140072206 | SEMANTIC REPRESENTATION MODULE OF A MACHINE LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM - 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. | 03-13-2014 |
20140132786 | IMAGE STABILIZATION TECHNIQUES FOR VIDEO SURVEILLANCE SYSTEMS - 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. | 05-15-2014 |
20150046155 | COGNITIVE NEURO-LINGUISTIC BEHAVIOR RECOGNITION SYSTEM FOR MULTI-SENSOR DATA FUSION - Embodiments presented herein describe techniques for generating a linguistic model of input data obtained from a data source (e.g., a video camera). According to one embodiment of the present disclosure, a sequence of symbols is generated based on an ordered stream of normalized vectors generated from the input data. A dictionary of words is generated from combinations of the ordered sequence of symbols based on a frequency at which combinations of symbols appear in the ordered sequence of symbols. A plurality of phrases is generated based an ordered sequence of words from the dictionary observed in the ordered sequence of symbols based on a frequency by which combinations of words in ordered sequence of words appear relative to one another. | 02-12-2015 |