Abstract: A computer implemented method for determining a vehicle type of a vehicle detected in an image is disclosed. An image having a detected vehicle is received. A number of vehicle models having salient feature points is projected on the detected vehicle. A first set of features derived from each of the salient feature locations of the vehicle models is compared to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a set of positive match scores (p-scores) and a set of negative match scores (n-scores). The detected vehicle is classified as one of the vehicle models models based at least in part on the set of p-scores and the set of n-scores.

Abstract: A system for object detection and tracking includes technologies to, among other things, detect and track moving objects, such as pedestrians and/or vehicles, in a real-world environment, handle static and dynamic occlusions, and continue tracking moving objects across the fields of view of multiple different cameras.

Abstract: A computing system for identifying, describing, and sharing salient events depicted in images and videos executes feature detection algorithms on multimedia input (e.g., video and/or images). The computing system applies semantic reasoning techniques to the output of the feature detection algorithms. The computing system identifies salient event segments of the multimedia input as a result of the semantic reasoning. The computing system can incorporate the salient event segments into a visual presentation, such as a video clip. Alternatively or in addition, the computing system can generate a natural language description of the content of the multimedia input.

Abstract: A computer-implemented method for matching objects is disclosed. At least two images where one of the at least two images has a first target object and a second of the at least two images has a second target object are received. At least one first patch from the first target object and at least one second patch from the second target object are extracted. A distance-based part encoding between each of the at least one first patch and the at least one second patch based upon a corresponding codebook of image parts including at least one of part type and pose is constructed. A viewpoint of one of the at least one first patch is warped to a viewpoint of the at least one second patch. A parts level similarity measure based on the view-invarient distance measure for each of the at least one first patch and the at least one second patch is applied to determine whether the first target object and the second target object are the same or different objects.

Abstract: A computer implemented method for detecting the presence of one or more pedestrians in the vicinity of the vehicle is disclosed. Imagery of a scene is received from at least one image capturing device. A depth map is derived from the imagery. A plurality of pedestrian candidate regions of interest (ROIs) is detected from the depth map by matching each of the plurality of ROIs with a 3D human shape model. At least a portion of the candidate ROIs is classified by employing a cascade of classifiers tuned for a plurality of depth bands and trained on a filtered representation of data within the portion of candidate ROIs to determine whether at least one pedestrian is proximal to the vehicle.

Abstract: A computer vision service includes technologies to, among other things, analyze computer vision or learning tasks requested by computer applications, select computer vision or learning algorithms to execute the requested tasks based on one or more performance capabilities of the computer vision or learning algorithms, perform the computer vision or learning tasks for the computer applications using the selected algorithms, and expose the results of performing the computer vision or learning tasks for use by the computer applications.

Abstract: The present invention relates to a method and apparatus for detecting and tracking vehicles. One embodiment of a system for detecting and tracking an object (e.g., vehicle) in a field of view includes a moving object indication stage for detecting a candidate object in a series of input video frames depicting the field of view and a track association stage that uses a joint probabilistic graph matching framework to associate an existing track with the candidate object.

Abstract: The present invention relates to a method and system for creating a strong classifier based on motion patterns wherein the strong classifier may be used to determine an action being performed by a body in motion. When creating the strong classifier, action classification is performed by measuring similarities between features within motion patterns. Embodiments of the present invention may utilize candidate part-based action sets and training samples to train one or more weak classifiers that are then used to create a strong classifier.

Abstract: A complex video event classification, search and retrieval system can generate a semantic representation of a video or of segments within the video, based on one or more complex events that are depicted in the video, without the need for manual tagging. The system can use the semantic representations to, among other things, provide enhanced video search and retrieval capabilities.

Abstract: A computer-implemented method comprising collecting data from a plurality of information sources, identifying a geographic location associated with the data and forming a corresponding event according to the geographic location, correlating the data and the event with one or more topics based at least partly on the identified geographic location and storing the correlated data and event and inferring the associated geographic location if the data does not comprise explicit location information, including matching the data against a database of geo-referenced data.

Abstract: A computer implemented method computer implemented method for deriving a fingerprint from video data is disclosed, comprising the steps of receiving a plurality of frames from the video data; selecting at least one key frame from the plurality of frames, the at least one key frame being selected from two consecutive frames of the plurality of frames that exhibiting a maximal cumulative difference in at least one spatial feature of the two consecutive frames; detecting at least one 3D spatio-temporal feature within the at least one key frame; and encoding a spatio-temporal fingerprint based on mean luminance of the at least one 3D spatio-temporal feature. The least one spatial feature can be intensity. The at least one 3D spatio-temporal feature can be at least one Maximally Stable Volume (MSV).

Abstract: The present invention provides a computer implemented process for detecting multi-view multi-pose objects. The process comprises training of a classifier for each intra-class exemplar, training of a strong classifier and combining the individual exemplar-based classifiers with a single objective function. This function is optimized using the two nested AdaBoost loops. The first loop is the outer loop that selects discriminative candidate exemplars. The second loop, the inner loop selects the discriminative candidate features on the selected exemplars to compute all weak classifiers for a specific position such as a view/pose. Then all the computed weak classifiers are automatically combined into a final classifier (strong classifier) which is the object to be detected.

Abstract: A computer implemented method for automatically detecting and classifying acoustic signatures across a set of recording conditions is disclosed. A first acoustic signature is received. The first acoustic signature is projected into a space of a minimal set of exemplars of acoustic signature types derived from a larger set of exemplars using a wrapper method. At least one vector distance is calculated between the projected acoustic signature and each exemplar of the minimal set of exemplars. An exemplar is selected from the minimal set of exemplars having the smallest vector distance to the projected acoustic signature as a class corresponding to and classifying the first acoustic signature. The first acoustic signature and the plurality of acoustic signatures may correspond to one of gunshots, musical instruments, songs, and speech. The minimal set of exemplars may correspond to a hierarchy of acoustic signature types.

Abstract: The present invention is a system and a method for processing stereo images utilizing a real time, robust, and accurate stereo matching system and method based on a coarse-to-fine architecture. At each image pyramid level, non-centered windows for matching and adaptive upsampling of coarse-level disparities are performed to generate estimated disparity maps using the ACTF approach. In order to minimize propagation of disparity errors from coarser to finer levels, the present invention performs an iterative optimization, at each level, that minimizes a cost function to generate smooth disparity maps with crisp occlusion boundaries.

Abstract: A computer-implemented method for estimating a volume of at least one food item on a food plate is disclosed. A first and second plurality of images are received from different positions above a food plate, wherein angular spacing between the positions of the first plurality of images is greater than angular spacing between the positions of the second plurality of images. A first set of poses of each of the first plurality of images is estimated. A second set of poses of each of the second plurality of images is estimated based on at least the first set of poses. A pair of images taken from each of the first and second plurality of images is rectified based on at least the first and second set of poses. A 3D point cloud is reconstructed based on at least the rectified pair of images. At least one surface of the at least one food item above the food plate is estimated based on at least the reconstructed 3D point cloud. The volume of the at least one food item is estimated based on the at least one surface.

Abstract: A method and apparatus for recognizing an object, comprising providing a set of scene features from a scene, pruning a set of model features, generating a set of hypotheses associated with the pruned set of model features for the set of scene features, pruning the set of hypotheses, and verifying the set of pruned hypotheses is provided.

Abstract: A method and apparatus for determining a geographic location of a scene in a captured depiction comprising extracting a first set of features from the captured depiction by algorithmically analyzing the captured depiction, matching the extracted features of the captured depiction against a second set of extracted features associated with reference depictions with known geographic locations and when the matching is successful, identifying the geographic location of the scene in the captured depiction based on a known geographic location of a matching reference depiction from the reference depictions.

Abstract: A computer-implemented method for for matching objects is disclosed. At least two images where one of the at least two images has a first target object and a second of the at least two images has a second target object are received. At least one first patch from the first target object and at least one second patch from the second target object are extracted. A distance-based part encoding between each of the at least one first patch and the at least one second patch based upon a corresponding codebook of image parts including at least one of part type and pose is constructed. A viewpoint of one of the at least one first patch is warped to a viewpoint of the at least one second patch. A parts level similarity measure based on the view-invariant distance measure for each of the at least one first patch and the at least one second patch is applied to determine whether the first target object and the second target object are the same or different objects.

Abstract: A method for automatically generating a strong classifier for determining whether at least one object is detected in at least one image is disclosed, comprising the steps of: (a) receiving a data set of training images having positive images; (b) randomly selecting a subset of positive images from the training images to create a set of candidate exemplars, wherein said positive images include at least one object of the same type as the object to be detected; (c) training a weak classifier based on at least one of the candidate exemplars, said training being based on at least one comparison of a plurality of heterogeneous compositional features located in the at least one image and corresponding heterogeneous compositional features in the one of set of candidate exemplars; (d) repeating steps (c) for each of the remaining candidate exemplars; and (e) combining the individual classifiers into a strong classifier, wherein the strong classifier is configured to determine the presence or absence in an image of the

Abstract: A method for extracting a 3D terrain model for identifying at least buildings and terrain from LIDAR data is disclosed, comprising the steps of generating a point cloud representing terrain and buildings mapped by LIDAR; classifying points in the point cloud, the point cloud having ground and non-ground points, the non-ground points representing buildings and clutter; segmenting the non-ground points into buildings and clutter; and calculating a fit between at least one building segment and at least one rectilinear structure, wherein the fit yields the rectilinear structure with the fewest number of vertices.