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公开(公告)号：US10237483B2
公开(公告)日：2019-03-19
申请号：US15582518
申请日：2017-04-28
Applicant: Omni AI, Inc.
Inventor： Kishor Adinath Saitwal , Wesley Kenneth Cobb , Tao Yang
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.
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公开(公告)号：US10096235B2
公开(公告)日：2018-10-09
申请号：US13839587
申请日：2013-03-15
Applicant: Omni AI, Inc.
Inventor： Wesley Kenneth Cobb , Ming-Jung Seow , Gang Xu , Kishor Adinath Saitwal , Anthony Akins , Kerry Joseph , Dennis G. Urech
IPC: H04N7/18 , G08B29/18 , G08B23/00 , G06K9/00 , H04N7/00 , G06K9/32 , G06K9/62 , G06K9/52 , G08B21/18 , G08B13/196
Abstract: Alert directives and focused alert directives allow a user to provide feedback to a behavioral recognition system to always or never publish an alert for certain events. Such an approach bypasses the normal publication methods of the behavioral recognition system yet does not obstruct the system's learning procedures.
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公开(公告)号：US10373340B2
公开(公告)日：2019-08-06
申请号：US15582558
申请日：2017-04-28
Applicant: OMNI AI, Inc.
Inventor： Kishor Adinath Saitwal , Lon W. Risinger , Wesley Kenneth Cobb
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.
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公开(公告)号：US10248869B2
公开(公告)日：2019-04-02
申请号：US15720971
申请日：2017-09-29
Applicant: OMNI AI, INC.
Inventor： Wesley Kenneth Cobb , Bobby Ernest Blythe , Rajkiran Kumar Gottumukkal , Kishor Adinath Saitwal , Gang Xu , Tao Yang
Abstract: 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.
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公开(公告)号：US09965382B2
公开(公告)日：2018-05-08
申请号：US15090366
申请日：2016-04-04
Applicant: Omni AI, Inc.
Inventor： Lon W. Risinger , Kishor Adinath Saitwal
CPC classification number: G06F12/023 , G06F3/061 , G06F3/0631 , G06F3/0644 , G06F3/067 , G06F2212/1016 , G06F2212/154 , G06F2212/263 , G06K9/00711 , G06K9/00993 , G06K9/66 , G06T1/20 , G06T1/60
Abstract: Techniques are disclosed for dynamic memory allocation in a behavioral recognition system. According to one embodiment of the disclosure, one or more variable-sized chunks of memory is allocated from a device memory for a memory pool. An application allocates at least one of the chunks of memory from the memory pool for processing a plurality of input data streams in real-time. A request to allocate memory from the memory pool for input data is received. Upon determining that one of the chunks is available in the memory pool to store the input data, the chunk is allocated from the memory pool in response to the request.
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Patent Agency Ranking
公开(公告)号：US10410058B1
公开(公告)日：2019-09-10
申请号：US15874172
申请日：2018-01-18
Applicant: Omni AI, Inc.
Inventor： Kishor Adinath Saitwal , Dennis G. Urech , Wesley Kenneth Cobb
Abstract: Techniques are disclosed for analyzing a scene depicted in an input stream of video frames captured by a video camera. The techniques include evaluating sequence pairs representing segments of object trajectories. Assuming the objects interact, each of the sequences of the sequence pair may be mapped to a sequence cluster of an adaptive resonance theory (ART) network. A rareness value for the pair of sequence clusters may be determined based on learned joint probabilities of sequence cluster pairs. A statistical anomaly model, which may be specific to an interaction type or general to a plurality of interaction types, is used to determine an anomaly temperature, and alerts are issued based at least on the anomaly temperature. In addition, the ART network and the statistical anomaly model are updated based on the current interaction.
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公开(公告)号：US10187415B2
公开(公告)日：2019-01-22
申请号：US15469568
申请日：2017-03-26
Applicant: Omni AI, Inc.
Inventor： Ming-Jung Seow , Wesley Kenneth Cobb , Gang Xu , Tao Yang , Aaron Poffenberger , Lon W. Risinger , Kishor Adinath Saitwal , Michael S. Yantosca , David M. Solum , Alex David Hemsath , Dennis G. Urech , Duy Trong Nguyen , Charles Richard Morgan
Abstract: 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.
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公开(公告)号：US10102642B2
公开(公告)日：2018-10-16
申请号：US14952090
申请日：2015-11-25
Applicant: Omni AI, Inc.
Inventor： Kishor Adinath Saitwal , Lon W. Risinger , Wesley Kenneth Cobb , Ming-Jung Seow , Gang Xu
Abstract: Techniques are disclosed for generating a low-dimensional representation of an image. An image driver receives an image captured by a camera. The image includes features based on pixel values in the image, and each feature describes the image in one or more image regions. The image driver generates, for each of the plurality of features, a feature vector that includes values for that feature corresponding to at least one of the image regions. Each value indicates a degree that the feature is present in the image region. The image driver generates a sample vector from each of the feature vectors. The sample vector includes each of the values included in the generated feature vectors.
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公开(公告)号：US10043100B2
公开(公告)日：2018-08-07
申请号：US15090795
申请日：2016-04-05
Applicant: Omni AI, Inc.
Inventor： Kishor Adinath Saitwal , Lon W. Risinger , Wesley Kenneth Cobb
Abstract: Techniques are disclosed for generating logical sensors for an image driver. The image driver monitors values corresponding to at least a first feature in one or more regions of a first image in a stream of images received by a first sensor. The image driver identifies at least a first correlation between at least a first and second value of the monitored values. The image driver generates a logical sensor based on the identified correlations. The logical sensor samples one or more features corresponding to the identified correlation from a second image in the stream of images.
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公开(公告)号：US09674442B2
公开(公告)日：2017-06-06
申请号：US14988475
申请日：2016-01-05
Applicant: OMNI AI, INC.
Inventor： Kishor Adinath Saitwal , Wesley Kenneth Cobb , Tao Yang
CPC classification number: H04N5/23267 , G06T7/246 , G06T2207/10016 , G06T2207/20021 , G06T2207/30232 , H04N5/23254 , H04N5/77
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.
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