Abstract: 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.

Abstract: An approach for infrastructure asset management is provided. This approach comprises an end-to-end analytics driven maintenance approach that can take data about physical assets and additional external data, and apply advanced analytics to the data to generate business insight, foresight and planning information. Specifically, this approach uses a maintenance analysis tool, which is configured to: receive data about a set of physical assets of an infrastructure, and analyze the data about the set of physical assets to predict maintenance requirements for each of the set of physical assets. The maintenance analysis tool further comprises an output component configured to generate a maintenance plan based on the predicted maintenance requirements for each of the set of physical assets.

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: An approach that detects locations of hazardous conditions within an infrastructure is provided. This approach uses satellite imagery, GIS data, automatic image processing, and predictive modeling to determine the location of the hazards automatically, thus optimizing infrastructure management. Specifically, a hazard detection tool provides this capability. The hazard detection tool comprises a detection component configured to: receive visual media containing asset location data about a set of physical assets, and hazard location data about potential hazards within a vicinity of each of the set of physical assets. The detection component further receives graphical information system (GIS) data containing asset location data about each of the set of physical assets.

Abstract: A preventative maintenance method and a system for estimating the risk of failure of an asset based on intrinsic parameters such as failure history combined with causative factors like weather and independent external risk factors such as vandalism and risk of flooding. The present invention may further have a system for estimating the risk of failure of an asset based on intrinsic parameters, such as failure history combined with causative factors such as weather and independent external risk factors like vandalism and risk of flooding having a location based asset/service failure risk estimator, an external risk estimates database for feeding and an integrated failure risk database, the external risk estimates database feeding the integrated failure risk database.

Abstract: A system and method that processes video to extract a keyframe-based adequate visual representation is disclosed. Certain embodiments utilize a hierarchical processing technique. A first stage in the hierarchy extracts a chromatic difference metric from a pair of video frames. An initial set of frames is chosen based on the chromatic metric and a threshold. A structural difference measurement is extracted from this initial set of frames. A second threshold is used to select key frames from the initial set. The first and second thresholds can be user selectable. The output of this process is the visual representation. The method is extensible to any number of metrics and any number of levels.

Abstract: 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.

Abstract: 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.

Abstract: An approach that detects objects crossing a virtual boundary line is provided. Specifically, an object detection tool provides this capability. The object detection tool comprises a boundary component configured to define a virtual boundary line in a video region of interest, and establish a set of ground patch regions surrounding the virtual boundary line. The object detection tool further comprises an extraction component configured to extract a set of attributes from each of the set of ground patch regions, and update a ground patch history model with the set of attributes from each of the set of ground patch regions. An analysis component is configured to analyze the ground patch history model to detect whether an object captured in at least one of the set of ground patch regions is crossing the virtual boundary line in the video region of interest.

Abstract: Techniques for analyzing one or more sequential events performed by a human actor to evaluate efficiency of the human actor are provided. The techniques include identifying one or more segments in a video sequence as one or more components of one or more sequential events performed by a human actor, integrating the one or more components into one or more sequential events by incorporating a spatiotemporal model and one or more event detectors, and analyzing the one or more sequential events to analyze behavior of the human actor.

Abstract: Determination of human behavior from an alignment of data streams includes acquiring visual image primitives from a video input comprising visual information relevant to a human activity. The primitives are temporally aligned to an optimally hypothesized sequence of primitives transformed from a sequence of transactions as a function of a distance metric between the observed primitive sequence and the transformed primitive sequence. More particularly, transforming includes comparing the distance metric costs and choosing and performing the lowest cost of temporally matching the observed primitives to one or more transactions, deleting a primitive, or associating a primitive with a pseudo transaction marker. Accordingly, alerts are issued based on analysis of the transformation of primitives.

Abstract: Human behavior alerts are determined from a video stream through application of video analytics that parse a video stream into a plurality of segments, wherein each of the segments are either temporally related to at least one of a plurality of temporally distinct transactions in an event data log; or they are each associated with a pseudo transaction marker if not temporally related to at least one of the temporally distinct transactions and an image analysis indicates a temporal correlation with at least one of the distinct transactions is expected. Visual image features are extracted from the segments and one-SVM classification is performed on the extracted features to categorize segments into inliers or outliers relative to a threshold boundary. Event of concern alerts are issued with respect to the inlier segments associated with the associated pseudo transaction marker.

Abstract: A system and method for monitoring events includes defining alert events based on primitive event types in an environment monitored by monitoring devices. Alert parameters are set for each alert event, and alert events are combined to create a composite alert event based on a plurality of alert events. A plurality of engines is monitored to determine if multiple events comprising the composite alert event have occurred.

Abstract: A method for determining a maintenance schedule of geographically dispersed physical assets includes receiving asset data including infrastructure relationships between the assets, modeling failure risk of the assets based on spatial, temporal and network relationships, and producing the maintenance schedule according to a combination of the risk model, asset data, maintenance, and external operation constraints. The maintenance schedule may be corrective and/or strategic.

Abstract: Multi-mode video event indexing includes determining a quality of object distinctiveness with respect to images from a video stream input. A high-quality analytic mode is selected from multiple modes and applied to video input images via a hardware device to determine object activity within the video input images if the determined level of detected quality of object distinctiveness meets a threshold level of quality, else a low-quality analytic mode is selected and applied to the video input images via a hardware device to determine object activity within the video input images, wherein the low-quality analytic mode is different from the high-quality analytic mode.

Abstract: 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.

Abstract: 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.

Abstract: The invention provides an improved method to detect semantic attributes of human body in computer vision. In detecting semantic attributes of human body in computer vision, the invention maintains a list of semantic attributes, each of which corresponds to a human body part. A computer module then analyzes segments of a frame of a digital video to detect each semantic attribute by finding a most likely attribute for each segment. A threshold is applied to select candidate segments of the frame for further analysis. The candidate segments of the frame then go through geometric and resolution context analysis by applying the physical structure principles of a human body and by analyzing increasingly higher resolution versions of the image to verify the existence and accuracy of parts and attributes.

Abstract: 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.

Abstract: 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.