Patent application number | Description | Published |
20080232681 | OBJECT DETECTION SYSTEM BASED ON A POOL OF ADAPTIVE FEATURES - A method, system and computer program product for detecting presence of an object in an image are disclosed. According to an embodiment, a method for detecting a presence of an object in an image comprises: receiving multiple training image samples; determining a set of adaptive features for each training image sample, the set of adaptive features matching the local structure of each training image sample; integrating the sets of adaptive features of the multiple training image samples to generate an adaptive feature pool; determining a general feature based on the adaptive feature pool; and examining the image using a classifier determined based on the general feature to detect the presence of the object. | 09-25-2008 |
20120026335 | Attribute-Based Person Tracking Across Multiple Cameras - Techniques for tracking an individual across two or more cameras are provided. The techniques include detecting an image of one or more individuals in each of two or more cameras, tracking each of the one or more individuals in a field of view in each of the two or more cameras, applying a set of one or more attribute detectors to each of the one or more individuals being tracked by the two or more cameras, and using the set of one or more attribute detectors to match an individual tracked in one of the two or more cameras with an individual tracked in one or more other cameras of the two or more cameras. | 02-02-2012 |
20120027248 | Foreground Analysis Based on Tracking Information - Techniques for performing foreground analysis are provided. The techniques include identifying a region of interest in a video scene, applying a background subtraction algorithm to the region of interest to detect a static foreground object in the region of interest, and determining whether the static foreground object is abandoned or removed, wherein determining whether the static foreground object is abandoned or removed comprises performing a foreground analysis based on edge energy and region growing, and pruning one or more false alarms using one or more track statistics. | 02-02-2012 |
20120027249 | Multispectral Detection of Personal Attributes for Video Surveillance - Techniques for detecting an attribute in video surveillance include generating training sets of multispectral images, generating a group of multispectral box features comprising receiving input of a detector size of a width and height, a number of spectral bands in the multispectral images, and integer values representing a minimum and maximum width and height of multispectral box features, fixing a feature width and to height, generating feature building blocks with the fixed width and height, placing a feature building block at a same location for each spectral band level, and enumerating combinations of the feature building blocks through each spectral level until all sizes within the integer values have been covered, and wherein each combination determines a multispectral box feature, using the training sets to select multispectral box features to generate a multispectral attribute detector, and using the multispectral attribute detector to identify a location of an attribute in video surveillance. | 02-02-2012 |
20120027297 | Object Segmentation at a Self-Checkout - Techniques for segmenting an object at a self-checkout are provided. The techniques include capturing an image of an object at a self-checkout, dividing the image into one or more blocks, computing one or more features of the image, computing a confidence value for each of the one or more blocks, wherein computing a confidence value for each of the one or more blocks comprises using a minimum feature distance from one or more reference backgound blocks, and eliminating one or more blocks from consideration via use of an adaptive threshold computed on the confidence value for each of the one or more blocks, wherein the one or more blocks remaining map to a region of the image containing the object. | 02-02-2012 |
20120030208 | Facilitating People Search in Video Surveillance - Techniques for facilitating a video surveillance search of a person are provided. The techniques include maintaining a database of one or more attributes of one or more people captured on one or more video cameras, indexing the one or more attributes in the database extracted from the one or more video cameras, and pruning one or more images captured from the one or more video cameras using the one or more attributes and one or more items of qualifying information to facilitate a video surveillance search of a person. | 02-02-2012 |
20120121170 | OBJECT DETECTION SYSTEM BASED ON A POOL OF ADAPTIVE FEATURES - A method, system and computer program product for detecting presence of an object in an image are disclosed. According to an embodiment, a method for detecting a presence of an object in an image comprises: receiving multiple training image samples; determining a set of adaptive features for each training image sample, the set of adaptive features matching the local structure of each training image sample; integrating the sets of adaptive features of the multiple training image samples to generate an adaptive feature pool; determining a general feature based on the adaptive feature pool; and examining the image using a classifier determined based on the general feature to detect the presence of the object. | 05-17-2012 |
20120170805 | OBJECT DETECTION IN CROWDED SCENES - Methods and systems are provided for object detection. A method includes automatically collecting a set of training data images from a plurality of images. The method further includes generating occluded images. The method also includes storing in a memory the generated occluded images as part of the set of training data images, and training an object detector using the set of training data images stored in the memory. The method additionally includes detecting an object using the object detector, the object detector detecting the object based on the set of training data images stored in the memory. | 07-05-2012 |
20120173301 | SYSTEM AND METHOD FOR FAILURE ASSOCIATION ANALYSIS - A system and method for mining the failure association rules of geographically dispersed physical assets is provided. One approach of the present invention has steps of joining input data sources, extracting spatio-temporal (ST) information, quantilizing ST continuous value in automated manner, or based on pre-built knowledge, applying association rule mining algorithm to find associations between attributes and failure and outputting identified ST failure association rules. | 07-05-2012 |
20120233159 | HIERARCHICAL RANKING OF FACIAL ATTRIBUTES - In response to a query of discernible facial attributes, the locations of distinct and different facial regions are estimated from face image data, each relevant to different attributes. Different features are extracted from the estimated facial regions from database facial images, which are ranked in base layer rankings by matching feature vectors in a bipartite graph to a base layer ranking sequence as a function of edge weights parameterized by an associated base layer parameter vector. Second-layer rankings define second-layer attribute vectors as bilinear combinations of the base-layer feature vectors and associated base layer parameter vectors for common attributes, which are matched in a bipartite graph to a second-layer ranking sequence as a function of edge weights parameterized by associated second-layer parameter vectors. The images are thus ranked for relevance to the query as a function of fusing the second-layer rankings. | 09-13-2012 |
20120263346 | VIDEO-BASED DETECTION OF MULTIPLE OBJECT TYPES UNDER VARYING POSES - Training data object images are clustered as a function of motion direction attributes and resized from respective original into same aspect ratios. Motionlet detectors are learned for each of the sets from features extracted from the resized object blobs. A deformable sliding window is applied to detect an object blob in input by varying window size, shape or aspect ratio to conform to a shape of the detected input video object blob. A motion direction of an underlying image patch of the detected input video object blob is extracted and motionlet detectors selected and applied that have similar motion directions. An object is thus detected within the detected blob and semantic attributes of an underlying image patch extracted if a motionlet detectors fires, the extracted semantic attributes available for use for searching for the detected object. | 10-18-2012 |
20120274805 | Color Correction for Static Cameras - Methods and apparatus are provided for color correction of images. One or more colors in an image obtained from a static video camera are corrected by obtaining one or more historical background models from one or more prior images obtained from the static video camera; obtaining a live background model and a live foreground model from one or more current images obtained from the static video camera; generating a reference image from the one or more historical background models; and processing the reference image, the live background model, and the live foreground model to generate a set of color corrected foreground objects in the image. The set of color corrected foreground objects is optionally processed to classify a color of at least one of the foreground objects. | 11-01-2012 |
20120281873 | INCORPORATING VIDEO META-DATA IN 3D MODELS - A moving object detected and tracked within a field of view environment of a 2D data feed of a calibrated video camera is represented by a 3D model through localizing a centroid of the object and determining an intersection with a ground-plane within the field of view environment. An appropriate 3D mesh-based volumetric model for the object is initialized by using a back-projection of a corresponding 2D image as a function of the centroid and the determined ground-plane intersection. Nonlinear dynamics of a tracked motion path of the object are represented as a collection of different local linear models. A texture of the object is projected onto the 3D model, and 2D tracks of the object are upgraded to 3D motion to drive the 3D model by learning a weighted combination of the different local linear models that minimizes an image re-projection error of model movement. | 11-08-2012 |
20120308121 | IMAGE RANKING BASED ON ATTRIBUTE CORRELATION - Images are retrieved and ranked according to relevance to attributes of a multi-attribute query through training image attribute detectors for different attributes annotated in a training dataset. Pair-wise correlations are learned between pairs of the annotated attributes from the training dataset of images. Image datasets may then be searched via the trained attribute detectors for images comprising attributes in a multi-attribute query, wherein images are retrieved from the searching that each comprise one or more of the query attributes and also in response to information from the trained attribute detectors corresponding to attributes that are not a part of the query but are relevant to the query attributes as a function of the learned plurality of pair-wise correlations. The retrieved images are ranked as a function of respective total numbers of attributes within the query subset attributes. | 12-06-2012 |
20120314030 | ESTIMATION OF OBJECT PROPERTIES IN 3D WORLD - Objects within two-dimensional (2D) video data are modeled by three-dimensional (3D) models as a function of object type and motion through manually calibrating a 2D image to the three spatial dimensions of a 3D modeling cube. Calibrated 3D locations of an object in motion in the 2D image field of view of a video data input are computed and used to determine a heading direction of the object as a function of the camera calibration and determined movement between the computed 3D locations. The 2D object image is replaced in the video data input with an object-type 3D polygonal model having a projected bounding box that best matches a bounding box of an image blob, the model oriented in the determined heading direction. The bounding box of the replacing model is then scaled to fit the object image blob bounding box, and rendered with extracted image features. | 12-13-2012 |
20130016877 | MULTI-VIEW OBJECT DETECTION USING APPEARANCE MODEL TRANSFER FROM SIMILAR SCENESAANM Feris; Rogerio S.AACI White PlainsAAST NYAACO USAAGP Feris; Rogerio S. White Plains NY USAANM Pankanti; Sharathchandra U.AACI DarienAAST CTAACO USAAGP Pankanti; Sharathchandra U. Darien CT USAANM Siddiquie; BehjatAACI College ParkAAST MDAACO USAAGP Siddiquie; Behjat College Park MD US - View-specific object detectors are learned as a function of scene geometry and object motion patterns. Motion directions are determined for object images extracted from a training dataset and collected from different camera scene viewpoints. The object images are categorized into clusters as a function of similarities of their determined motion directions, the object images in each cluster are acquired from the same camera scene viewpoint. Zenith angles are estimated for object image poses in the clusters relative to a position of a horizon in the cluster camera scene viewpoint, and azimuth angles of the poses as a function of a relation of the determined motion directions of the clustered images to the cluster camera scene viewpoint. Detectors are thus built for recognizing objects in input video, one for each of the clusters, and associated with the estimated zenith angles and azimuth angles of the poses of the respective clusters. | 01-17-2013 |
20130101208 | BACKGROUND UNDERSTANDING IN VIDEO DATA - Long-term understanding of background modeling includes determining first and second dimension gradient model derivatives of image brightness data of an image pixel along respective dimensions of two-dimensional, single channel image brightness data of a static image scene. The determined gradients are averaged with previous determined gradients of the image pixels, and with gradients of neighboring pixels as a function of their respective distances to the image pixel, the averaging generating averaged pixel gradient models for each of a plurality of pixels of the video image data of the static image scene that each have mean values and weight values. Background models for the static image scene are constructed as a function of the averaged pixel gradients and weights, wherein the background model pixels are represented by averaged pixel gradient models having similar orientation and magnitude and weights meeting a threshold weight requirement. | 04-25-2013 |
20130108102 | Abandoned Object Recognition Using Pedestrian Detection | 05-02-2013 |
20130124514 | HIERARCHICAL RANKING OF FACIAL ATTRIBUTES - In response to a query of discernable facial attributes, the locations of distinct and different facial regions are estimated from face image data, each relevant to different attributes. Different features are extracted from the estimated facial regions from database facial images, which are ranked in base layer rankings by matching feature vectors to a base layer ranking sequence as a function of edge weights. Second-layer rankings define second-layer attribute vectors as combinations of the base-layer feature vectors and associated base layer parameter vectors for common attributes, which are matched to a second-layer ranking sequence as a function of edge weights. The images are thus ranked for relevance to the query as a function of the second-layer rankings. | 05-16-2013 |
20130230239 | Object Segmentation at a Self-Checkout - Techniques for segmenting an object at a self-checkout are provided. The techniques include capturing an image of an object at a self-checkout, dividing the image into one or more blocks, computing a confidence value for each of the one or more blocks, and eliminating one or more blocks from consideration based on the confidence value for each of the one or more blocks, wherein the one or more blocks remaining map to a region of the image containing the object. | 09-05-2013 |
20130241928 | INCORPORATING VIDEO META-DATA IN 3D MODELS - A moving object detected and tracked within a field of view environment of a two-dimensional data feed of a calibrated video camera is represented by a three-dimensional model through localizing a centroid of the object and determining an intersection with a ground-plane within the field of view environment. An appropriate three-dimensional mesh-based volumetric model for the object is initialized by using a back-projection of a corresponding two-dimensional image as a function of the centroid and the determined ground-plane intersection. A texture of the object is projected onto the three-dimensional model, and two-dimensional tracks of the object are upgraded to three-dimensional motion to drive a three-dimensional model. | 09-19-2013 |
20130243254 | Foreground Analysis Based on Tracking Information - Techniques for performing foreground analysis are provided. The techniques include identifying a region of interest in a video scene, detecting a static foreground object in the region of interest, and determining whether the static foreground object is abandoned or removed, wherein said determining comprises performing a foreground analysis based on tracking information and pruning one or more false alarms using one or more track statistics. | 09-19-2013 |
20130243256 | Multispectral Detection of Personal Attributes for Video Surveillance - Techniques, systems, and articles of manufacture for multispectral detection of attributes for video surveillance. A method includes generating one or more training sets of one or more multispectral images, generating a group of one or more multispectral box features, using the one or more training sets to select one or more of the one or more multispectral box features to generate a multispectral attribute detector, and using the multispectral attribute detector to identify a location of an attribute in video surveillance, wherein using the multispectral attribute detector comprises, for one or more locations on each spectral band level of the multispectral image, applying the multispectral attribute detector and producing an output indicating attribute detection or an output indicating no attribute detection, and wherein the attribute corresponds to the multispectral attribute detector. | 09-19-2013 |
20130272573 | MULTI-VIEW OBJECT DETECTION USING APPEARANCE MODEL TRANSFER FROM SIMILAR SCENES - View-specific object detectors are learned as a function of scene geometry and object motion patterns. Motion directions are determined for object images extracted from a training dataset and collected from different camera scene viewpoints. The object images are categorized into clusters as a function of similarities of their determined motion directions, the object images in each cluster are acquired from the same camera scene viewpoint. Zenith angles are estimated for object image poses in the clusters relative to a position of a horizon in the cluster camera scene viewpoint, and azimuth angles of the poses as a function of a relation of the determined motion directions of the clustered images to the cluster camera scene viewpoint. Detectors are thus built for recognizing objects in input video, one for each of the clusters, and associated with the estimated zenith angles and azimuth angles of the poses of the respective clusters. | 10-17-2013 |
20130336581 | MULTI-CUE OBJECT DETECTION AND ANALYSIS - Foreground objects of interest are distinguished from a background model by dividing a region of interest of a video data image into a grid array of individual cells that are each smaller than that a foreground object of interest. More particularly, image data of the foreground object of interest spans a contiguous plurality of the cells. Each of the cells are labeled as foreground if accumulated edge energy within the cell meets an edge energy threshold, if color intensities for different colors within each cell differ by a color intensity differential threshold, or as a function of combinations of said determinations in view of one or more combination rules. | 12-19-2013 |
20140003708 | OBJECT RETRIEVAL IN VIDEO DATA USING COMPLEMENTARY DETECTORS | 01-02-2014 |
20140003724 | DETECTION OF STATIC OBJECT ON THOROUGHFARE CROSSINGS | 01-02-2014 |
20140056476 | INCORPORATING VIDEO META-DATA IN 3D MODELS - A moving object tracked within a field of view environment of a two-dimensional data feed of a calibrated video camera is represented by a three-dimensional model. An appropriate three-dimensional mesh-based volumetric model for the object is initialized by using a back-projection of a corresponding two-dimensional image. A texture of the object is projected onto the three-dimensional model, and two-dimensional tracks of the object are upgraded to three-dimensional motion to drive a three-dimensional model. | 02-27-2014 |
20140056479 | DETERMINATION OF TRAIN PRESENCE AND MOTION STATE IN RAILWAY ENVIRONMENTS - Foreground feature data and motion feature data is determined for frames of video data acquired from a train track area region of interest. The frames are labeled as “train present” if the determined foreground feature data value meets a threshold value, else as “train absent; and as “motion present” if the motion feature data meets a motion threshold, else as “static.” The labels are used to classify segments of the video data comprising groups of consecutive video frames, namely as within a “no train present” segment for groups with “train absent” and “static” labels; within a “train present and in transition” segment for groups “train present” and “motion present” labels; and within a “train present and stopped” segment for groups with “train present” and “static” labels. The presence or motion state of a train at a time of inquiry is thereby determined from the respective segment classification. | 02-27-2014 |
20140098221 | APPEARANCE MODELING FOR OBJECT RE-IDENTIFICATION USING WEIGHTED BRIGHTNESS TRANSFER FUNCTIONS - An approach for re-identifying an object in a first test image is presented. Brightness transfer functions (BTFs) between respective pairs of training images are determined. Respective similarity measures are determined between the first test image and each of the training images captured by the first camera (first training images). A weighted brightness transfer function (WBTF) is determined by combining the BTFs weighted by weights of the first training images. The weights are based on the similarity measures. The first test image is transformed by the WBTF to better match one of the training images captured by the second camera. Another test image, captured by the second camera, is identified because it is closer in appearance to the transformed test image than other test images captured by the second camera. An object in the identified test image is a re-identification of the object in the first test image. | 04-10-2014 |
20140098989 | MULTI-CUE OBJECT ASSOCIATION - Multiple discrete objects within a scene image captured by a single camera track are distinguished as un-labeled from a background model within a first frame of a video data input. Object position and object appearance and/or object size attributes are determined for each of the blobs, and costs determined to assign to existing blobs of existing object tracks as a function of the determined attributes and combined to generate respective combination costs. The un-labeled object blob that has a lowest combined cost of association with any of the existing object tracks is labeled with the label of that track having the lowest combined cost, said track is removed from consideration for labeling remaining un-labeled object blobs, and the process iteratively repeated until each of the track labels have been used to label one of the un-labeled blobs. | 04-10-2014 |
20140122470 | HIERARCHICAL RANKING OF FACIAL ATTRIBUTES - In response to a query of discernable facial attributes, the locations of distinct and different facial regions are estimated from face image data, each relevant to different attributes. Different features are extracted from the estimated facial regions from database facial images, which are ranked in base layer rankings as a function of relevance of extracted features to attributes relevant to the estimated regions, and in second-layer rankings as a function of combinations of the base layer rankings and relevance of the extracted features to common ones of the attributes relevant to the estimated regions. The images are ranked in relevance to the query as a function of the second-layer rankings. | 05-01-2014 |
20140153779 | Object Segmentation at a Self-Checkout - Techniques for segmenting an object are provided. The techniques include capturing an image of an object, dividing the image into one or more blocks, computing a confidence value for each of the one or more blocks, and eliminating one or more blocks from consideration based on the confidence value for each of the one or more blocks. | 06-05-2014 |
20140267738 | VISUAL MONITORING OF QUEUES USING AUXILLARY DEVICES - Methods and system are provided for monitoring a queue. A method includes receiving by sensors a non-visual identifier at predefined locations of a queue. Further, the method includes capturing by image capture devices images of an object possessing the non-visual identifier at the predefined locations of the queue. Further, the method includes visually tracking another object in the queue based on transformations of a predefined feature extracted from the images of the object possessing the non-visual identifier at the predefined locations. | 09-18-2014 |
20140314277 | INCORPORATING VIDEO META-DATA IN 3D MODELS - A moving object tracked within a field of view environment of a two-dimensional data feed of a calibrated video camera is represented by a three-dimensional model. An appropriate three-dimensional mesh-based volumetric model for the object is initialized by using a back-projection of a corresponding two-dimensional image. A texture of the object is projected onto the three-dimensional model, and two-dimensional tracks of the object are upgraded to three-dimensional motion to drive a three-dimensional model. | 10-23-2014 |