Abstract: The present application discloses a method and apparatus for detecting a vehicle contour based on point cloud data. The method includes: acquiring to-be-trained point cloud data; generating label data corresponding to points in the to-be-trained point cloud data in response to labeling on the points in the to-be-trained point cloud, the labeling used to indicate whether each of the points in the to-be-trained point cloud data is on a vehicle contour; training a fully convolutional neural network model based on the points in the to-be-trained point cloud data and the label data corresponding to the points in the to-be-trained point cloud data, to obtain a vehicle detection model; and acquiring to-be-detected point cloud data, and obtaining a detection result corresponding to each to-be-detected point in the to-be-detected point cloud data based on the vehicle detection model. The implementation may achieve an accurate detection of the vehicle contour.

Abstract: Systems and methods are disclosed for deep learning and classifying images of objects by receiving images of objects for training or classification of the objects; producing fine-grained labels of the objects; providing object images to a multi-class convolutional neural network (CNN) having a softmax layer and a final fully connected layer to explicitly model bipartite-graph labels (BGLs); and optimizing the CNN with global back-propagation.

Abstract: A computer-implemented method for detecting objects by using subcategory-aware convolutional neural networks (CNNs) is presented. The method includes generating object region proposals from an image by a region proposal network (RPN) which utilizes subcategory information, and classifying and refining the object region proposals by an object detection network (ODN) that simultaneously performs object category classification, subcategory classification, and bounding box regression. The image is an image pyramid used as input to the RPN and the ODN. The RPN and the ODN each include a feature extrapolating layer to detect object categories with scale variations among the objects.

Abstract: The present application discloses a method and apparatus for detecting a vehicle contour based on point cloud data. The method includes: acquiring to-be-trained point cloud data; generating label data corresponding to points in the to-be-trained point cloud data in response to labeling on the points in the to-be-trained point cloud, the labeling used to indicate whether each of the points in the to-be-trained point cloud data is on a vehicle contour; training a fully convolutional neural network model based on the points in the to-be-trained point cloud data and the label data corresponding to the points in the to-be-trained point cloud data, to obtain a vehicle detection model; and acquiring to-be-detected point cloud data, and obtaining a detection result corresponding to each to-be-detected point in the to-be-detected point cloud data based on the vehicle detection model. The implementation may achieve an accurate detection of the vehicle contour.

Abstract: Systems and methods are disclosed for classifying vehicles by performing scale aware detection; performing detection assisted sampling for convolutional neural network (CNN) training, and performing deep CNN fine-grained image classification to classify the vehicle type.

Abstract: A computer-implemented method for training a convolutional neural network (CNN) is presented. The method includes receiving regions of interest from an image, generating one or more convolutional layers from the image, each of the one or more convolutional layers having at least one convolutional feature within a region of interest, applying at least one cascaded rejection classifier to the regions of interest to generate a subset of the regions of interest, and applying scale dependent pooling to convolutional features within the subset to determine a likelihood of an object category.

Abstract: A computer-implemented method for detecting objects by using subcategory-aware convolutional neural networks (CNNs) is presented. The method includes generating object region proposals from an image by a region proposal network (RPN) which utilizes subcategory information, and classifying and refining the object region proposals by an object detection network (ODN) that simultaneously performs object category classification, subcategory classification, and bounding box regression. The image is an image pyramid used as input to the RPN and the ODN. The RPN and the ODN each include a feature extrapolating layer to detect object categories with scale variations among the objects.

Abstract: A method to reconstruct 3D model of an object includes receiving with a processor a set of training data including images of the object from various viewpoints; learning a prior comprised of a mean shape describing a commonality of shapes across a category and a set of weighted anchor points encoding similarities between instances in appearance and spatial consistency; matching anchor points across instances to enable learning a mean shape for the category; and modeling the shape of an object instance as a warped version of a category mean, along with instance-specific details.

Abstract: Systems and methods are disclosed for deep learning and classifying images of objects by receiving images of objects for training or classification of the objects; producing fine-grained labels of the objects; providing object images to a multi-class convolutional neural network (CNN) having a softmax layer and a final fully connected layer to explicitly model bipartite-graph labels (BGLs); and optimizing the CNN with global back-propagation.

Abstract: Methods and systems for distance metric learning include generating two random projection matrices of a dataset from a d-dimensional space into an m-dimensional sub-space, where m is smaller than d. An optimization problem is solved in the m-dimensional subspace to learn a distance metric based on the random projection matrices. The distance metric is recovered in the d-dimensional space.

Abstract: Systems and methods are disclosed for classifying vehicles by performing scale aware detection; performing detection assisted sampling for convolutional neural network (CNN) training, and performing deep CNN fine-grained image classification to classify the vehicle type.

Abstract: Systems and methods are disclosed for training a learning machine by augmenting data from fine-grained image recognition with labeled data annotated by one or more hyper-classes, performing multi-task deep learning; allowing fine-grained classification and hyper-class classification to share and learn the same feature layers; and applying regularization in the multi-task deep learning to exploit one or more relationships between the fine-grained classes and the hyper-classes.

Abstract: An object detector includes a bottom-up object hypotheses generation unit; a top-down object search with supervised descent unit; and an object re-localization unit with a localization model.

Abstract: An object detector includes a bottom-up object hypotheses generation unit; a top-down object search with supervised descent unit; and an object re-localization unit with a localization model.

Abstract: Systems and methods are disclosed for detecting an object in an image by determining convolutional neural network responses on the image; mapping the responses back to their spatial locations in the image; and constructing features densely extract shift invariant activations of a convolutional neural network to produce dense features for the image.

Abstract: Systems and methods are disclosed to respond to a query for one or more images by using a processor, applying an indexing strategy which processes images as grouplets rather than individual single images; generating a two layer indexing structure with a group layer, each associated with one or more images in an image layer; cross-indexing the images into two or more groups; and retrieving near duplicate images with the cross-indexed images and the grouplets.

Abstract: Systems and methods are disclosed for autonomous driving with only a single camera by moving object localization in 3D with a real-time framework that harnesses object detection and monocular structure from motion (SFM) through the ground plane estimation; tracking feature points on moving cars a real-time framework to and use the feature points for 3D orientation estimation; and correcting scale drift with ground plane estimation that combines cues from sparse features and dense stereo visual data.

Abstract: Systems and methods are disclosed for object detection by receiving an image and extracting features therefrom; applying a learning process to determine sub-regions and select predetermined pooling regions; and performing selective max-pooling to choose one or more feature regions without noises.

Abstract: Methods and systems for distance metric learning include generating two random projection matrices of a dataset from a d-dimensional space into an m-dimensional sub-space, where m is smaller than d. An optimization problem is solved in the m-dimensional subspace to learn a distance metric based on the random projection matrices. The distance metric is recovered in the d-dimensional space.

Abstract: Systems and methods are disclosed for detecting an object in an image by determining convolutional neural network responses on the image; mapping the responses back to their spatial locations in the image; and constructing features densely extract shift invariant activations of a convolutional neural network to produce dense features for the image.