Abstract: Embodiments of the present invention provide a system, method, and program product to determine whether a product has been successfully purchased by identifying in a video record when a movement of a product adjacent to a scanner occurs, and whether the scanner did not record a purchase transaction at that time; measuring a difference in time between the time of the movement of the product and a time of another movement of a product, and determining by a trained support vector machine a likelihood that the product was successfully purchased. Alternately, the difference in time can be measured between the time of the movement of the product and a time of a transaction record, or between the time of the movement of the product and a boundary time. The support vector machine can use a radial basis function kernel and can generate a decision value and a confidence score.

Abstract: A method includes obtaining two-dimensional measurements of a given scene from sensors, the given scene comprising a plurality of scene points, distributing the given scene into bundles each associated with at least one sensor and at least one scene point, establishing constraints associated with the sensors and the plurality of scene points that are associated with two different bundles, estimating, individually for each of the bundles, a set of parameters for the at least one sensor and the at least one scene point associated with that bundle utilizing at least one of the two-dimensional measurements, generating a consensus parameter set for the one or more sensors and the plurality of scene points based on the estimated parameter sets, the established constraints being utilized to reconcile differences in parameter values in the estimated parameter sets, and producing a three-dimensional representation of the given scene utilizing the consensus parameter set.

Abstract: A computer-implemented method includes receiving one or more training images depicting one or more training geographical regions. One or more environmental characteristic (EC) values are determined for the training images. The EC values include at least one EC value for each of the training images. One or more models are generated for mapping an EC value of an image to a determination of whether an artifact is present in a geographical region depicted by the image, based on the EC values and based on knowledge of which of the training images depict training geographical regions having artifacts present. A new image is received depicting a new geographical region. The models are applied to the new image. A probability that a new artifact is present in the new geographical region depicted in the new image is determined, based on the applying the models to the new image.

Abstract: A discrepancy detection system for detecting a discrepancy between a plurality of video images of a same scene captured by an imaging device at different times, the system including a correspondence point detection device configured to detect corresponding points matching each other between a first video image sequence of the plurality of video images and a second video image sequence of the plurality of video images and a comparison device configured to compare image content of the first video image sequence and the second video image sequence for the discrepancy in the image content between the first video image sequence and the second video image sequence.

Abstract: An aerial drone is coupled to a warning sign for warning other vehicles of a presence of a faulty vehicle. The aerial drone is positioned at a location of the faulty vehicle, in response to detecting the faulty vehicle. The aerial drone assesses environmental conditions at the location of the faulty vehicle. An optimal position for positioning the warning sign is determined, based on the environmental conditions at the location of the faulty vehicle, and the aerial drone is positioned at the optimal position.

Abstract: An approach for re-identifying an object in a test image is presented. Similarity measures between the test image and training images captured by a first camera are determined. The similarity measures are based on Bhattacharyya distances between feature representations of an estimated background region of the test image and feature representations of background regions of the training images. A transformed test image based on the Bhattacharyya distances has a brightness that is different from the test image's brightness, and matches a brightness of training images captured by a second camera. An appearance of the transformed test image resembles an appearance of a capture of the test image by the second camera. Another image included in test images captured by the second camera is identified as being closest in appearance to the transformed test image and another object in the identified other image is a re-identification of the object.

Abstract: Machine logic that pre-processes and post-processes images for visual object detection by performing the following steps: receiving a set of image(s); filtering the set of image(s) using a set of multimodal integral filter(s), thereby removing at least a portion of the set of image(s) and resulting in a filtered set of image(s); performing object detection on the filtered set of image(s) to generate a set of object-detected image(s); assembling a first plurality of object-detected image(s) from the set of object-detected image(s); and upon assembling the first plurality of object-detected image(s), performing non-maximum suppression on the assembled first plurality of object-detected image(s).

Abstract: A method, system and computer program product for shielding an operator of a vehicle from light, including determining a position of a light source based on the location of the vehicle and the current date and time. A line of sight between the light source and the eyes of the operator is determined, where the line of sight goes through a windshield of the vehicle that includes a plurality of pixels. One or more pixels in the line of sight are identified; and, one or more physical properties of the pixel(s) in the line of sight are modified.

Abstract: A computer-implemented method modifies physical classroom resources in a classroom. One or more processors identify and quantify physical classroom resources in the classroom based on sensor readings received from sensors in a classroom. The processor(s) determine physical classroom resource constraints that impede learning by students in the classroom based on the sensor readings from the sensors in the classroom. The processor(s) detect one or more of the physical classroom resource constraints in the physical classroom resources identified by the sensor readings, and then adjust the one or more physical classroom resources based on the one or more detected physical classroom resource constraints.

Abstract: A data indexing system and method includes acquiring activity data in a context and indexing the activity data in accordance with contextual conditions. The activity data is stored in accordance with indices. An event is correlated with the activity data by using the indices to review the activity data in the context.

Abstract: Technical solutions are described for training an object-recognition neural network that identifies an object in a computer-readable image. An example method includes assigning a first neural network for determining a visual alignment model of the images for determining a normalized alignment of the object. The method further includes assigning a second neural network for determining a visual representation model of the images for recognizing the object. The method further includes determining the visual alignment model by training the first neural network and determining the visual representation model by training the second neural network independent of the first. The method further includes determining a combined object recognition model by training a combination of the first neural network and the second neural network. The method further includes recognizing the object in the image based on the combined object recognition model by passing the image through each of the neural networks.

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: A method of operating an image detection device includes receiving an image, dividing the image into a plurality of patches, grouping ones of the plurality of patches, generating a set of saccadic paths through the plurality of patches of the image, generating a cluster-direction sequence for each saccadic path, generating a policy function for identifying an object in a new image using a combination of the cluster-direction sequences, and operating the image detection device using the policy function to identify an object in the new image.

Abstract: A method includes the following steps. A video sequence including detection results from one or more detectors is received, the detection results identifying one or more objects. A clustering framework is applied to the detection results to identify one or more clusters associated with the one or more objects. The clustering framework is applied to the video sequence on a frame-by-frame basis. Spatial and temporal information for each of the one or more clusters are determined. The one or more clusters are associated to the detection results based on the spatial and temporal information in consecutive frames of the video sequence to generate tracking information. One or more target tracks are generated based on the tracking information for the one or more clusters. The one or more target tracks are consolidated to generate refined tracks for the one or more objects.

Abstract: A computer implemented method for detecting an object in a crowded scene utilizing an image capturing device. The method includes receiving an image of a predetermined area. From the image, the existence of selected portions as representing an entity of a selected class is determined. Each selected portion is assigned an initial confidence value that the selected portion is an entity representative of a selected class. Each selected portion is evaluated with each other to determine a context confidence value. The context confidence value and initial confidence value are utilized to determine which of the one or more selected portions are entities of a selected class.

Abstract: A method of operating an image detection device includes receiving an image, dividing the image into a plurality of patches, grouping ones of the plurality of patches, generating a set of saccadic paths through the plurality of patches of the image, generating a cluster-direction sequence for each saccadic path, generating a policy function for identifying an object in a new image using a combination of the cluster-direction sequences, and operating the image detection device using the policy function to identify an object in the new image.

Abstract: A method, system and computer program product for shielding an operator of a vehicle from light, including determining a position of a light source based on the location of the vehicle and the current date and time. A line of sight between the light source and the eyes of the operator is determined, where the line of sight goes through a windshield of the vehicle that includes a plurality of pixels. One or more pixels in the line of sight are identified; and, one or more physical properties of the pixel(s) in the line of sight are modified.

Abstract: Aspects determining anomalous events, wherein processors determine a trajectory of tracked movement of an object through an image field of a camera partitioned into a matrix grid of different local units. The aspects generate anomaly confidence decision values for image features extracted from video data of the tracked movement of the object as a function of fitting extracted image features to normal patterns of local motion pattern models defined by dominant distributions of extracted image features. The aspects further extract trajectory features from the video data relative to the trajectory of the tracked movement of the object, and generate global anomaly confidence decision values for the object trajectory as a function of fitting the extracted trajectory features to a normal learned motion trajectory model. The aspects determine anomalous events as a function of the generated global anomaly confidence decision value and the anomaly confidence decision values.

Abstract: Local models learned from anomaly detection are used to rank detected anomalies. The local model patterns are defined from 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 fitting extracted image features to the local model patterns. 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: An aerial drone is coupled to a warning sign for warning other vehicles of a presence of a faulty vehicle. The aerial drone is positioned at a location of the faulty vehicle, in response to detecting the faulty vehicle. The aerial drone assesses environmental conditions at the location of the faulty vehicle. An optimal position for positioning the warning sign is determined, based on the environmental conditions at the location of the faulty vehicle, and the aerial drone is positioned at the optimal position.