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: Automated analysis of video data for determination of human behavior includes segmenting a video stream into a plurality of discrete individual frame image primitives which are combined into a visual event that may encompass an activity of concern as a function of a hypothesis. The visual event is optimized by setting a binary variable to true or false as a function of one or more constraints. The visual event is processed in view of associated non-video transaction data and the binary variable by associating the visual event with a logged transaction if associable, issuing an alert if the binary variable is true and the visual event is not associable with the logged transaction, and dropping the visual event if the binary variable is false and the visual event is not associable.

Abstract: 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.

Abstract: Various embodiments allow scanning by a shopper using a barcode reader (e.g., a scanner) attached to or positioned near the shopping receptacle. As items are scanned, they are identified based on their barcode and added to an item list. Item verification can then performed at checkout using imaging technology. For example, the shopping cart or shopping basket can be brought into the field of view of a computer-connected camera. The camera and computer can, working from the customer's item list developed when the items are scanned, observe each product in the receptacle and ring it up. If all products can be accounted for, the customer is free to leave; otherwise the customer is denied egress, informed of the problem, etc. A store employee can also be signaled to investigate.

Abstract: 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.

Abstract: 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.

Abstract: The present invention provides a smart scanning system comprising an integrated scanning and image capture system in which one or more image capture device(s) (e.g., still camera, video camera, etc.) and a barcode scanner are positioned within a common enclosure that is a component of a checkout station. The barcode of item is scanned and an image of the item is recorded. The identity of the item as determined based on the barcode is compared to its appearance as determined based on its image. If the identity is inconsistent with its appearance, a discrepancy is registered. It is then determined whether the discrepancy is due to fraud (e.g., theft) or device error. In the case of the latter, the system can be updated to prevent a repeat of the error.

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 method and system for training a special object detector to distinguish a foreground object appearing in a sequence of frames for a target domain. The sequence of frames depicts motion of the foreground object in a non-uniform background. The foreground object is detected in a high-confidence subwindow of an initial frame of the sequence, which includes computing a measure of confidence that the high-confidence subwindow includes the foreground object and determining that the measure of confidence exceeds a specified confidence threshold. The foreground object is tracked in respective positive subwindows of subsequent frames appearing after the initial frame. The subsequent frames are within a specified short period of time. The positive subwindows are used to train the special object detector to detect the foreground object in the target domain. The positive subwindows include the subwindow of the initial frame and the respective subwindows of the subsequent frames.

Abstract: Automated analysis of video data for determination of human behavior includes segmenting a video stream into a plurality of discrete individual frame image primitives which are combined into a visual event that may encompass an activity of concern as a function of a hypothesis. The visual event is optimized by setting a binary variable to true or false as a function of one or more constraints. The visual event is processed in view of associated non-video transaction data and the binary variable by associating the visual event with a logged transaction if associable, issuing an alert if the binary variable is true and the visual event is not associable with the logged transaction, and dropping the visual event if the binary variable is false and the visual event is not associable.

Abstract: Human behavior is determined by sequential event detection by constructing a temporal-event graph with vertices representing primitive images of images of a video stream, and also of idle states associated with the respective primitive images. A human activity event is determined as a function of a shortest distance path of the temporal-event graph vertices.

Abstract: A computer generates a three dimensional map of a pathway area using a plurality of overhead images. The computer determines a forecasted weather pattern to occur in the pathway area. The computer analyzes the three dimensional map and the forecasted weather pattern to predict one or more violations of the pathway. The computer generates a priority for the one or more predicted violations of the pathway. The computer generates a plan for pathway management of the pathway area.

Abstract: Under the present invention, item verification is automated and expedited. Specifically, items to be purchased can be scanned by the shopper using a barcode reader (e.g., a scanner) attached to or positioned near the shopping receptacle. As items are scanned, they are identified based on their barcode and added to an item list. Item verification can then performed at checkout using imaging technology. For example, the shopping cart or shopping basket can be brought into the field of view of a computer-connected camera. The camera and computer can, working from the customer's item list developed when the items are scanned, observe each product in the receptacle and “ring it up”. If all products can be accounted for, the customer is free to leave; otherwise the customer is denied egress, informed of the problem, etc. A store employee can also be signaled to investigate.

Abstract: A method, data processing system, and computer program product for identifying abnormalities in data. A model representing a plurality of modes for an activity generated from training data is retrieved. The training data includes a first plurality of measurements of a first performance of the activity over a period of time. Each of the plurality of modes is identified as one of normal and abnormal. Activity data including a second plurality of measurements of a second performance of the activity is received. A portion of the activity data is compared with the plurality of modes in the model. A notification of an abnormality in the second performance of the activity is generated in response to an identification that the portion of the activity data matches a mode in the plurality of modes identified as abnormal. Confirmation of the abnormality is requested via a user interface.

Abstract: A foreground object blob having a bounding box detected in frame image data is classified by a finite state machine as a background, moving foreground, or temporally static object, namely as the temporally static object when the detected bounding box is distinguished from a background model of a scene image of the video data input and remains static in the scene image for a threshold period. The bounding box is tracked through matching masks in subsequent frame data of the video data input, and the object sub-classified within a visible sub-state, an occluded sub-state, or another sub-state that is not visible and not occluded as a function of a static value ratio. The ratio is a number of pixels determined to be static by tracking in a foreground region of the background model corresponding to the tracked object bounding box over a total number of pixels of the foreground region.

Abstract: Color-correcting a digital image comprising P pixels (P?4) is presented. Each of the P pixels has a respective color. Color strengths of the P pixels are determined based at least on respective intensities, respective saturations, or both respective intensities and respective saturations of the P pixels. A subset of the P pixels less than all of the P pixels is determined. The pixels in the subset have respective color strengths in a range of respective color strength. All other pixels of the P pixels have respective color strengths outside of the range of respective color strengths. Color correction is determined for the P pixels based in part on the colors of the respective pixels in the subset which are the only pixels of the P pixels used for determining the color correction. The colors of the P pixels are corrected based on the color correction.

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: 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.

Abstract: 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.

Abstract: An approach that automatically distinguishes between in-store customers and in-store employees is provided. In one embodiment, there is a learning tool configured to construct a model for an in-store employee; a matching tool configured to match attributes between a particular person and the constructed models for an in-store employee; and a classifying tool configured to classify persons into categories of employees and customers based on amount of matching attributes between a particular person and the model for an in-store employee.