Sri International

Sang-Hack Jung

Harpreet Singh Sawhney

Ajay Divakaran

Rakesh Kumar

Hui Ting Cheng

Bogdan Calin Mihai Matei

Jayan Eledath

Dennis Lee Matthies

Feng Han

Jiangjian Xiao

Saad Masood Khan

Chris Broaddus

Supun Samarasekera

Mayank Bansal

John Benjamin Southall

Yanlin Guo

Raia Hadsell

Garbis Salgian

Vlad Branzoi

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 based at least in part on the set of p-scores and the set of n-scores.

3-D model based method for detecting and classifying vehicles in aerial imagery

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.

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.

Method and apparatus for real-time pedestrian detection for urban driving

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.

Method for pose invariant fingerprinting

The present invention relates to a system and method for detecting one or more targets belonging to a first class (e.g., moving and/or stationary people), from a moving platform in a 3D-rich environment. The framework described here is implemented using a number of monocular or stereo cameras distributed around the vehicle to provide 360 degrees coverage. Furthermore, the framework described here utilizes numerous filters to reduce the number of false positive identifications of the targets.

System and method for object detection from a moving platform

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.

Real-time action detection and classification

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.

Method for pose invariant vessel fingerprinting

A method for estimating egomotion of a camera mounted on a vehicle that uses infra-red images is disclosed, comprising the steps of (a) receiving a pair of frames from a plurality of frames from the camera, the first frame being assigned to a previous frame and an anchor frame and the second frame being assigned to a current frame; (b) extracting features from the previous frame and the current frame; (c) finding correspondances between extracted features from the previous frame and the current frame; and (d) estimating the relative pose of the camera by minimizing reprojection errors from the correspondences to the anchor frame. The method can further comprise the steps of (e) assigning the current frame as the anchor frame when a predetermined amount of image motion between the current frame and the anchor frame is observed; (f) assigning the current frame to the previous frame and assigning a new frame from the plurality of frames to the current frame; and (g) repeating steps (b)-(f) until there are no more frames from the plurality of frames to process. Step (c) is based on an estimation of the focus of expansion between the previous frame and the current frame.

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Lawrenceville, NJ, United States of America

Sang-Hack Jung