Abstract: Alerts to object behaviors are prioritized for adjudication as a function of relative values of abandonment, foregroundness and staticness attributes. The attributes are determined from feature data extracted from video frame image data. The abandonment attribute indicates a level of likelihood of abandonment of an object. The foregroundness attribute quantifies a level of separation of foreground image data of the object from a background model of the image scene. The staticness attribute quantifies a level of stability of dimensions of a bounding box of the object over time. Alerts are also prioritized according to an importance or relevance value that is learned and generated from the relative abandonment, foregroundness and staticness attribute strengths.

Abstract: Video analytics data is audited through review of selective subsets of visual images from a visual image stream as a function of a temporal relationship of the images to a triggering alert event. The subset comprehends an image contemporaneous with the triggering alert event and one or more other images occurring before or after the contemporaneous image. The generated subset may be presented for review to determine whether the triggering alert event is a true or false alert, or whether additional data from the visual image stream is required to make such a determination. If determined from the presented visual essence that the additional data is required to make the true or false determination, then additional data is presented from the visual image stream for review.

Abstract: 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 are searched via the trained attribute detectors for images comprising attributes in a multi-attribute query. The retrieved images are ranked as a function of comprising attributes that are not within the query subset plurality of attributes but are paired to one of the query subset plurality of attributes by the pair-wise correlations, wherein the ranking is an order of likelihood that the different ones of the attributes will appear in an image with the paired one of the query subset plurality of attributes.

Abstract: 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.

Abstract: Alerts to object behaviors are prioritized for adjudication as a function of relative values of abandonment, foregroundness and staticness attributes. The attributes are determined from feature data extracted from video frame image data. The abandonment attribute indicates a level of likelihood of abandonment of an object. The foregroundness attribute quantifies a level of separation of foreground image data of the object from a background model of the image scene. The staticness attribute quantifies a level of stability of dimensions of a bounding box of the object over time. Alerts are also prioritized according to an importance or relevance value that is learned and generated from the relative abandonment, foregroundness and staticness attribute strengths.

Abstract: A computer identifies a proto-object in a digital image using a background subtraction method, the proto-object being associated with a lighting artifact in the surveillance region. The background subtraction method preserves boundary details and interior texture details of proto-objects associated with lighting artifacts. A plurality of characteristics of the proto-object digital data are determined, the characteristics, individually or in combination, distinguish a proto-object related to a lighting artifact from its background. A learning machine, trained with the plurality of characteristics of proto-objects classified as either foreground or not foreground, determines a likelihood that the plurality of characteristics is associated with a foreground object.

Abstract: A camera at a fixed vertical height positioned above a reference plane, with an axis of a camera lens at an acute angle with respect to a perpendicular of the reference plane. One or more processors receive images of different people. The vertical measurement values of the images of different people are determined. The one or more processors determine a first statistical measure associated with a statistical distribution of the vertical measurement values. The known heights of people from a known statistical distribution of heights of people are transformed to normalized measurements, based in part on a focal length of the camera lens, the angle of the camera, and a division operator in an objective function of differences between the normalized measurements and the vertical measurement values. The fixed vertical height of the camera is determined, based at least on minimizing the objective function.

Abstract: Alerts to object behaviors are prioritized for adjudication as a function of relative values of abandonment, foregroundness and staticness attributes. The attributes are determined from feature data extracted from video frame image data. The abandonment attribute indicates a level of likelihood of abandonment of an object. The foregroundness attribute quantifies a level of separation of foreground image data of the object from a background model of the image scene. The staticness attribute quantifies a level of stability of dimensions of a bounding box of the object over time. Alerts are also prioritized according to an importance or relevance value that is learned and generated from the relative abandonment, foregroundness and staticness attribute strengths.

Abstract: Image-matching tracks the movements of the objects from initial camera scenes to ending camera scenes in non-overlapping cameras. Paths are defined through scenes for pairings of initial and ending cameras by different respective scene entry and exit points. For each of said camera pairings a combination path having a highest total number of tracked movements relative to all other combinations of one path through the initial and ending camera scene is chosen, and the scene exit point of the selected path through the initial camera and the scene entry point of the selected path into the ending camera define a path connection of the initial camera scene to the ending camera scene.

Abstract: Software for static object detection that performs the following operations: (i) detecting an object that is present in at least one image of a set of images, wherein the set of images correspond to a time period; (ii) identifying a set of corner points for the detected object; (iii) tracking the object's presence in the set of images over the time period, wherein the object's presence is determined by matching the set of images to a template generated based on the identified corner points; and (iv) identifying the object as a static object when an amount of time corresponding to the object's presence in the set of images is greater than a predefined threshold.

Abstract: Video image data is acquired from synchronized cameras having overlapping views of objects moving past the cameras through a scene image in a linear array and with a determined speed. Processing units generate one or more object detections associated with confidence scores within frames of the camera video stream data. The confidence scores are modified as a function of constraint contexts including a cross-frame constraint that is defined by other confidence scores of other object detection decisions from the video data that are acquired by the same camera at different times; a cross-view constraint defined by other confidence scores of other object detections in the video data from another camera with an overlapping field-of-view; and a cross-object constraint defined by a sequential context of a linear array of the objects, spatial attributes of the objects and the determined speed of the movement of the objects relative to the cameras.

Abstract: 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 are searched via the trained attribute detectors for images comprising attributes in a multi-attribute query. The retrieved images are ranked as a function of comprising attributes that are not within the query subset plurality of attributes but are paired to one of the query subset plurality of attributes by the pair-wise correlations, wherein the ranking is an order of likelihood that the different ones of the attributes will appear in an image with the paired one of the query subset plurality of attributes.

Abstract: Transaction units of video data and transaction data captured from different checkout lanes are prioritized as a function of lane priority values of respective ones of the different checkout lanes from which the transaction units are acquired. Each of the checkout lanes has a different lane priority value. The individual transaction units are processed in the prioritized processing order to automatically detect irregular activities indicated by the transaction unit video and the transaction data of the processed individual transaction units.

Abstract: Automatic object retrieval from input video is based on learned, complementary detectors created for each of a plurality of different motionlet clusters. The motionlet clusters are partitioned from a dataset of training vehicle images as a function of determining that vehicles within each of the scenes of the images in each cluster share similar two-dimensional motion direction attributes within their scenes. To train the complementary detectors, a first detector is trained on motion blobs of vehicle objects detected and collected within each of the training dataset vehicle images within the motionlet cluster via a background modeling process; a second detector is trained on each of the training dataset vehicle images within the motionlet cluster that have motion blobs of the vehicle objects but are misclassified by the first detector; and the training repeats until all of the training dataset vehicle images have been eliminated as false positives or correctly classified.

Abstract: 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. Each of the cells are labeled as foreground if accumulated edge energy within the cell meets an edge energy threshold, or 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.

Abstract: Alerts to object behaviors are prioritized for adjudication as a function of relative values of abandonment, foregroundness and staticness attributes. The attributes are determined from feature data extracted from video frame image data. The abandonment attribute indicates a level of likelihood of abandonment of an object. The foregroundness attribute quantifies a level of separation of foreground image data of the object from a background model of the image scene. The staticness attribute quantifies a level of stability of dimensions of a bounding box of the object over time. Alerts are also prioritized according to an importance or relevance value that is learned and generated from the relative abandonment, foregroundness and staticness attribute strengths.

Abstract: Local models learned from anomaly detection are used to rank detected anomalies. The local models include image feature values extracted from an image field of video image data with respect to different predefined spatial and temporal local units, wherein anomaly results are determined by failures to fit to applied anomaly detection module local models. Image features values extracted from the image field local units associated with anomaly results are normalized, and image feature values extracted from the image field local units are clustered. Weights for anomaly results are learned as a function of the relations of the normalized extracted image feature values to the clustered image feature values. The normalized values are multiplied by the learned weights to generate ranking values to rank the anomalies.

Abstract: Field of view overlap among multiple cameras are automatically determined as a function of the temporal overlap of object tracks determined within their fields-of-view. Object tracks with the highest similarity value are assigned into pairs, and portions of the assigned object track pairs having a temporally overlapping period of time are determined. Scene entry points are determined from object locations on the tracks at a beginning of the temporally overlapping period of time, and scene exit points from object locations at an ending of the temporally overlapping period of time. Boundary lines for the overlapping fields-of-view portions within the corresponding camera fields-of-view are defined as a function of the determined entry and exit points in their respective fields-of-view.

Abstract: 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.

Abstract: Transaction units of video data and transaction data captured from different checkout lanes are prioritized as a function of lane priority values of respective ones of the different checkout lanes from which the transaction units are acquired. Each of the checkout lanes has a different lane priority value. The individual transaction units are processed in the prioritized processing order to automatically detect irregular activities indicated by the transaction unit video and the transaction data of the processed individual transaction units.