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John Eric Eaton, Wesley Kenneth Cobb, Bobby Ernest Blythe, Rajkiran Kumar Gottumukkal, Kishor Adinath Saitwal: Context processor for video analysis system. Behavioral Recognition Systems, October 11, 2012: US20120257831-A1

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 object ...


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Takuhara Masahito, Tomonaka Tetsuya, Sugimoto Kiichi: Behavior recognition system. Mitsubishi Heavy, August 11, 2005: JP2005-215927

PROBLEM TO BE SOLVED: To provide a behavior recognition system capable of appropriately recognizing daily actions such as 'eating' and 'drinking' not taking a fixed pattern and differing in a sequence and frequency every time.SOLUTION: Recognition processing in processing picture data on a photograp ...


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Shin Jun Ho, Kim Dae Hwan, Lee Sang Hak, Kim Yong Ho: Automatic sensitivity control-type human behavior recognition system for controlling sensitivity automatically under various conditions and situations. Korea Electronics Technology Institute, January 8, 2009: KR1020070026100

PURPOSE: An automatic sensitivity control-type human behavior recognition system is provided to collect human behavior information without an error, and to offer a service matched with a more exact behavior, thereby calculating more-reliable momentum and an amount of metabolism. CONSTITUTION: More t ...


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IDENTIFYING ANOMALOUS OBJECT TYPES DURING CLASSIFICATION. BEHAVIORAL RECOGNITION SYSTEMS, January 24, 2013: US20130022242-A1

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 ...


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BEHAVIOR RECOGNITION SYSTEM. March 13, 2014: US20140071037-A1

A system for recognizing various human and creature motion gaits and behaviors is presented. These behaviors are defined as combinations of “gestures” identified on various parts of a body in motion. For example, the leg gestures generated when a person runs are different than when a person walks. T ...


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COGNITIVE NEURO-LINGUISTIC BEHAVIOR RECOGNITION SYSTEM FOR MULTI-SENSOR DATA FUSION. February 12, 2015: US20150046155-A1

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 fr ...


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PERCEPTUAL ASSOCIATIVE MEMORY FOR A NEURO-LINGUISTIC BEHAVIOR RECOGNITION SYSTEM. June 16, 2016: US20160170961-A1

Techniques are disclosed for generating a syntax for a neuro-linguistic model of input data obtained from one or more sources. A stream of words of a dictionary built from a sequence of symbols are received. The symbols are generated from an ordered stream of normalized vectors generated from input ...


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LEXICAL ANALYZER FOR A NEURO-LINGUISTIC BEHAVIOR RECOGNITION SYSTEM. June 16, 2016: US20160170964-A1

Techniques are disclosed for building a dictionary of words from combinations of symbols generated based on input data. A neuro-linguistic behavior recognition system includes a neuro-linguistic module that generates a linguistic model that describes data input from a source (e.g., video data, SCADA ...


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MAPPER COMPONENT FOR A NEURO-LINGUISTIC BEHAVIOR RECOGNITION SYSTEM. June 16, 2016: US20160171096-A1

Techniques are disclosed for generating a sequence of symbols based on input data for a neuro-linguistic model. The model may be used by a behavior recognition system to analyze the input data. A mapper component of a neuro-linguistic module in the behavior recognition system receives one or more no ...