Abstract: A system, method, and computer program product for detecting anomalies in an image. In an example embodiment the method includes partitioning each image of a set of images into a plurality of image local units. The method further includes clustering all local units in the image set into clusters, and consequently assigning a class label to each local unit based on the clustering results. The local units with identical class labels having at least one substantially related image feature. Further, the method includes assigning a weight to each of the local units based on a variation of the class labels across all images in a set of images. The method further includes performing a clustering over all images in the set by using a distance metric that takes the learned weight of each local unit into account, then determining the images that belong to minorities of the clusters as anomalies.

Abstract: A system and computer program product for tracking and monitoring assets along a transport route. The system includes at least one receiver for receiving asset identifications transmitted from the assets, where each asset transmits its own asset identification. The receiver also receives physical location coordinates of each of the assets. A plurality of cameras is dispersed along the transport route for transmitting camera images of the assets. The system further includes a server coupled to the receiver and cameras. The server is configured to recognize the assets in the camera images, to correlate the asset identification from the receiver with recognized assets in the camera images, and to correlate the physical location coordinates of each of the assets with physical location coordinates of the cameras. The system monitors the visual appearance of the assets, and keeps track of whether or not they have sustained physical damage.

Abstract: Visual content in images captured from a scene by a camera in each of a plurality of different pose settings are analyzed to determine predicted occurrences of a transaction associated with the visual content in each pose, which are compared with actual transaction occurrence data to generate performance values for each pose as a function difference between the predicted and actual transactions. Optimized poses are chosen having the best performance value, wherein a camera controller may place the camera in the optimum pose for use in monitoring the scene and generating the primitives of interest associated with the transactions.

Abstract: A global position of an observed object is determined by obtaining a first global position of an observed object with at least one positioning device. A determination is made as to whether a set of stored visual characteristic information of at least one landmark matches a visual characteristic information set obtained from at least one captured image comprising a scene associated with the observed object. In response to the set of stored visual characteristic information matching the obtained visual characteristic information set, a second global position of the observed object is determined based on a set of stored location information associated with the at least one landmark and the first global position.

Abstract: A global position of an observed object is determined by obtaining a first global position of an observed object with at least one positioning device. A determination is made as to whether a set of stored visual characteristic information of at least one landmark matches a visual characteristic information set obtained from at least one captured image comprising a scene associated with the observed object. In response to the set of stored visual characteristic information matching the obtained visual characteristic information set, a second global position of the observed object is determined based on a set of stored location information associated with the at least one landmark and the first global position.

Abstract: A method, system, and computer program product for automatically inspecting railroad tracks. The method includes assessing a configuration of rail components depicted in an image by comparing the configuration of the rail components to known hazards. The method also includes determining a severity of detected problems in the configuration of the rail components, using a computer processor.

Abstract: 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.

Abstract: A method to detect objects in a digital image. At least one image representing at least one frame of a video sequence is received. A given color channel of the image is extracted. At least one blob that stands out from a background of the given color channel is identified. One or more features are extracted from the blob. The one or more features are provided to a plurality of pre-learned object models each including a set of pre-defined features associated with a pre-defined blob type. The one or more features are compared to the set of pre-defined features. The blob is determined to be of a type that substantially matches a pre-defined blob type associated with one of the pre-learned object models. At least a location of an object is visually indicated within the image that corresponds to the blob.

Abstract: A system and method to detect objects in a digital image. At least one image representing at least one frame of a video sequence is received. A given color channel of the image is extracted. At least one blob that stands out from a background of the given color channel is identified. One or more features are extracted from the blob. The one or more features are provided to a plurality of pre-learned object models each including a set of pre-defined features associated with a pre-defined blob type. The one or more features are compared to the set of pre-defined features. The blob is determined to be of a type that substantially matches a pre-defined blob type associated with one of the pre-learned object models. At least a location of an object is visually indicated within the image that corresponds to the blob.

Abstract: There is provided an apparatus for the certification of privacy compliance. The apparatus includes a registry of at least one of enrolled video surveillance operators, approved surveillance hardware devices, approved surveillance software programs, approved surveillance system installers, and approved entities that manage surveillance systems. The apparatus further includes a registry searcher, in signal communication with the registry, for receiving queries to the registry, and for determining whether at least one of a particular surveillance operator, a particular surveillance hardware device, a particular surveillance software program, a particular surveillance system installer, and a particular entity that manages a particular surveillance system is on the registry based on a given query.

Abstract: A system and method for intelligently controlling headlights receive a multiplicity of images that represent frames of a video sequence of an external environment of a vehicle. At least one bright spot, or blob, is found that stands out from a dark background of the external environment within each frame of the multiplicity of images. A multiplicity of features is extracted from a found blob. A type is recognized of a found blob that is selected from a multiplicity of types of blobs. A determination is then made whether to turn on a high beam light or a low beam light based at least on the recognized type of the found blob and a set of rules. Finally, an action based on such decision is performed.

Abstract: A method, information processing system, and computer program storage product annotate video images associated with an environmental situation based on detected actions of a human interacting with the environmental situation. A set of real-time video images are received that are captured by at least one video camera associated with an environment presenting one or more environmental situations to a human. One or more user actions made by the human that is associated with the set of real-time video images with respect to the environmental situation are monitored. A determination is made, based on the monitoring, that the human driver has one of performed and failed to perform at least one action associated with one or more images of the set of real-time video images. The one or more images of the set of real-time video images are annotated with a set of annotations.

Abstract: Techniques 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 background 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.

Abstract: An imaging system includes an image capturing device and a plurality of reflective devices. The image capturing device is configured to receive a plurality of images reflected by the plurality of reflective devices. Responsive to receiving the plurality of images, the image capturing device is further configured to capture within a single frame at least a first image corresponding to a first side of a first railroad track rail, a second image corresponding to a second side of the first railroad track rail, a third image corresponding to a first side of a second railroad track rail, and a fourth image corresponding to a second side of the second railroad track rail.

Abstract: A system and method to detect objects in a digital image. At least one image representing at least one frame of a video sequence is received. A sliding window of different window sizes at different locations is placed in the image. A cascaded classifier including a plurality of increasingly accurate layers is applied to each window size and each location. Each layer includes a plurality of classifiers. An area of the image within a current sliding window is evaluated using one or more weak classifiers in the plurality of classifiers based on at least one of Haar features and Histograms of Oriented Gradients features. An output of each weak classifier is a weak decision as to whether the area of the image includes an instance of an object of a desired object type. A location of the zero or more images associated with the desired object type is identified.

Abstract: A system, method, and computer program product for detecting anomalies in an image. In an example embodiment the method includes partitioning each image of a set of images into a plurality of image local units. The method further includes clustering all local units in the image set into clusters, and consequently assigning a class label to each local unit based on the clustering results. The local units with identical class labels having at least one substantially related image feature. Further, the method includes assigning a weight to each of the local units based on a variation of the class labels across all images in a set of images. The method further includes performing a clustering over all images in the set by using a distance metric that takes the learned weight of each local unit into account, then determining the images that belong to minorities of the clusters as anomalies.

Abstract: A system and method detect objects in a digital image. At least positional data associated with a vehicle is received. Geographical information associated with the positional data is received. A probability of detecting a target object within a corresponding geographic area associated with the vehicle is determined based on the geographical data. The probability is compared to a given threshold. An object detection process is at least one of activated and maintained in an activated state in response to an object detection process in response to the probability being one of above and equal to the given threshold. The object detection process detects target objects within at least one image representing at least one frame of a video sequence of an external environment. The object detection process is at least one of deactivated and maintained in a deactivated state in response to the probability being below the given threshold.

Abstract: A method to detect objects in a digital image. At least one image representing at least one frame of a video sequence is received. A given color channel of the image is extracted. At least one blob that stands out from a background of the given color channel is identified. One or more features are extracted from the blob. The one or more features are provided to a plurality of pre-learned object models each including a set of pre-defined features associated with a pre-defined blob type. The one or more features are compared to the set of pre-defined features. The blob is determined to be of a type that substantially matches a pre-defined blob type associated with one of the pre-learned object models. At least a location of an object is visually indicated within the image that corresponds to the blob.

Abstract: Techniques for performing visual surveillance of one or more moving objects are provided. The techniques include registering one or more images captured by one or more cameras, wherein registering the one or more images comprises region-based registration of the one or more images in two or more adjacent frames, performing motion segmentation of the one or more images to detect one or more moving objects and one or more background regions in the one or more images, and tracking the one or more moving objects to facilitate visual surveillance of the one or more moving objects.

Abstract: A system for automatically acquiring high-resolution images by steering a pan-tilt-zoom camera at targets detected in a fixed camera view is provided. The system uses automatic or manual calibration between multiple cameras. Using automatic calibration, the homography between the cameras in a home position is estimated together with the effects of pan and tilt controls and the expected height of a person in the image. These calibrations are chained together to steer a slave camera. The manual calibration scheme steers a camera to the desired region of interest and calculates the pan, tile and zoom parameters accordingly.