Abstract: A system for assessing a property damage claim uses an imaging device to capture images of a property that has reportedly been damaged by an incident. The system receives a property damage incident type, along with image processing criteria for processing images that are associated with the incident type. The system automatically processes the images according to the image processing criteria to identify one or more characteristics in the images and, based on the identified characteristics, determines whether certain claim processing criteria are satisfied. The claim processing criteria may help the system determine whether the property actually was damaged by the reported incident. The system will generate a command to process the property damage claim only if the system determines that one or more images show that the claim processing criteria are satisfied.

Abstract: A system for capturing property damage images includes an imaging device and a knowledge base of property damage incident types. The knowledge base also includes, for each category, one or more image acquisition parameters that are associated with the category. A processor receives a property damage incident type for a property damage claim that is associated with a property that reportedly experienced an incident, retrieves from the knowledge base one or more image acquisition parameters that are associated with the received incident type, and uses the one or more applicable image acquisition parameters to automatically cause the imaging device to capture digital images of the property using the retrieved one or more image acquisition parameters.

Abstract: Systems and methods are disclosed for background modeling in a computer vision system for enabling foreground object detection. A video acquisition model receives video data from a sequence of frames. A fit test module identifies a foreground object from the video data and defines a foreground mask representative of the identified foreground object. A foreground-aware background estimation module defines a first background model from the video data and then further defines an updated background model from an association of a current frame of the video data, the first background model and the foreground mask.

Abstract: A method and system for identifying content relevance comprises acquiring video data, mapping the acquired video data to a feature space to obtain a feature representation of the video data, assigning the acquired video data to at least one action class based on the feature representation of the video data, and determining a relevance of the acquired video data.

Abstract: A system, method, and apparatus for determining risk associated with a plurality of loan accounts, having an off-line mode and an online mode. In the off-line mode a first plurality of account histories is received. A maximum value variable m is set. A definition is received of a predetermined maximum look-ahead timeframe p. An iterative variable i is set equal to zero. While i is less than the maximum value variable m, a plurality of variables associated with an account history equaling the iterative variable i are stored and i incremented by 1. A predictive multi-output risk model is trained. In the online mode, a second plurality of account histories is received. A determination is made which accounts have a future risk level greater than a current risk level, and a further determination made which accounts currently require one or more tasks. Accounts requiring tasks are automatically assigned.

Abstract: Block-based motion estimation of video compression estimates the direction and magnitude of motion of objects in the scene in a computationally efficient manner and accurately predicts the optimal search direction/neighborhood location for motion vectors. A system can include a motion detection module that detects apparent motion in the scene, a motion direction and magnitude prediction module that estimates the direction and magnitude of motion of the objects detected to be in motion by the motion detection module, and a block-based motion estimation module that performs searches in reduced neighborhoods of the target block according to the estimated motion by the motion direction and magnitude prediction module and only for the blocks determined to be in motion by the motion detection module. The Invention is particularly well suited for stationary traffic cameras that monitor roads and highways for traffic law enforcement purposes.

Abstract: A multimodal sensing system includes various devices that work together to automatically classify an action. A video camera captures a sequence of digital images. At least one other sensor device captures other sensed data (e.g., motion data). The system will extract video features from the digital images so that each extracted image feature is associated with a time period. It will extract other features from the other sensed data so that each extracted other feature is associated with a time period. The system will fuse a group of the extracted video features and a group of the extracted other features to create a fused feature representation for a time period. It will then analyze the fused feature representation to identify a class, access a data store of classes and actions to identify an action that is associated with the class, and save the identified action to a memory device.

Abstract: This disclosure provides a method and system for automatically recognizing facial expressions at variable resolutions of video. According to one exemplary method, facial expressions are detected, extracted and classified from a video sequence based on an automatic localization of the periocular region associated with a detected and extracted face.

Abstract: A system and method of monitoring a region of interest comprises obtaining visual data comprising image frames of the region of interest over a period of time, analyzing individual subjects within the region of interest, the analyzing including at least one of tracking movement of individual subjects over time within the region of interest or extracting an appearance attribute of the individual subjects, and defining a group to include individual subjects having at least one of similar movement profiles or similar appearance attributes. The tracking movement includes detecting at least one of a trajectory of an individual subject within the region of interest, a dwell of an individual subject in at least one location within the region of interest, or an entrance or exit location within the region of interest.

Abstract: Methods and systems for automatically synchronizing videos acquired via two or more cameras with overlapping views in a multi-camera network. Reference lines within an overlapping field of view of the two (or more) cameras in the multi-camera network can be determined wherein the reference lines connect two or more pairs of corresponding points. Spatiotemporal maps of the reference lines can then be obtained. An optimal alignment between video segments obtained from the cameras is then determined based on the registration of the spatiotemporal maps.

Abstract: A system and method for object tracking and timing across multiple camera views includes local and global tracking modules for tracking the location of objects as they traverse particular regions of interest within an area of interest. A local timing module measures the time spent with each object within the area captured by a camera. A global timing module measures the time taken by the tracked object to traverse the entire area of interest or the length of the stay of the object within the area of interest.

Abstract: A method and system for reconstructing an image of a scene comprises configuring a digital light modulator according to a spatially varying pattern. Light energy associated with the scene and incident on the spatially varying pattern is collected and optically focused on the photodetectors. Data indicative of the intensity of the focused light energy from each of said at least two photodetectors is collected. Data from the photodetectors is then combined to reconstruct an image of the scene.

Abstract: This disclosure provides a method and system for automatically recognizing facial expressions at variable resolutions of video. According to one exemplary method, facial expressions are detected, extracted and classified from a video sequence based on an automatic localization of the periocular region associated with a detected and extracted face.

Abstract: A method, computer readable medium and apparatus for verifying an identity of an individual based upon facial expressions as exhibited in a query video of the individual are disclosed. The method includes receiving a reference video for each one of a plurality of different individuals, wherein a plurality of facial gesture encoders is extracted from at least one frame of the reference video describing one or more facial expressions of each one of the plurality of different individuals, receiving the query video, calculating a similarity score for the reference video for the each one of the plurality of different individuals based on an analysis that compares the plurality of facial gesture encoders of the at least one frame of the reference video for the each one of the plurality of different individuals to a plurality of facial gesture encoders extracted from at least one frame of the query video.

Abstract: A method, computer readable medium and apparatus for verifying an identity of an individual based upon facial expressions as exhibited in a query video of the individual are disclosed. The method includes receiving a reference video for each one of a plurality of different individuals, wherein a plurality of facial gesture encoders is extracted from at least one frame of the reference video describing one or more facial expressions of each one of the plurality of different individuals, receiving the query video, calculating a similarity score for the reference video for the each one of the plurality of different individuals based on an analysis that compares the plurality of facial gesture encoders of the at least one frame of the reference video for the each one of the plurality of different individuals to a plurality of facial gesture encoders extracted from at least one frame of the query video.

Abstract: Automated low-complexity video-based vehicle speed estimation is described, that operates within the video stream to screen video sequences to identify and eliminate clear non-violators and/or identify and select potential violators within a multi-layer speed enforcement system, in which deeper layers provide enhanced accuracy on selected candidate (speeding) vehicles. Video motion vector clusters corresponding to a vehicle are identified and tracked across multiple frames of captured video. Movement of the motion vector clusters translated from pixels per second to real speed (e.g. miles per hour) to determine whether the vehicle was speeding. Estimated speed data is added to the video stream data is metadata, and video segments of candidate speeding vehicles are stored and/or transmitted for subsequent review (e.g. automated or manual).

Abstract: A method for idling vehicle detection comprises shining a light source on an exterior surface of a vehicle with an illumination module, collecting light reflected from the exterior surface of the vehicle with a capture module, and processing the collected light reflected from the exterior surface of the vehicle surface with a processing module wherein the processing model determines if the vehicle's engine is running or if the vehicle's engine is not running.

Abstract: In a system for detecting location of an object inside of a building, an image capture device of a mobile electronic device captures an image of a boundary of a room in which the portable electronic device is positioned. The system extracts features of a boundary (ceiling, wall or floor) in the image to determine whether the mobile device is in a known location. When the system identifies a known location, it will take an action that provides the portable electronic device with additional functionality at the identified known location. Such functionality may include connecting to a wireless network or communicating with a stationary device at the known location.

Abstract: This disclosure provides a method and system for automated sequencing of vehicles in side-by-side drive-thru configurations via appearance-based classification. According to an exemplary embodiment, an automated sequencing method includes computer-implemented method of automated sequencing of vehicles in a side-by-side drive-thru, the method comprising: a) an image capturing device capturing video of a merge-point area associated with multiple lanes of traffic merging; b) detecting in the video a vehicle as it traverses the merge-point area; c) classifying the detected vehicle associated with traversing the merge-point area as coming from one of the merging lanes; and d) aggregating vehicle classifications performed in step c) to generate a merge sequence of detected vehicles.

Abstract: A system and method of providing annotated trajectories by receiving image frames from a video camera and determining a location based on the image frames from the video camera. The system and method can further include the steps of determining that the location is associated with a preexisting annotation and displaying the preexisting annotation. Additionally or alternatively, the system and method can further include the steps of generating a new annotation automatically or based on a user input and associating the new annotation with the current location.