Abstract: A vehicular image synthesis apparatus include a plurality of cameras mounted on a vehicle for capturing an image of a driver. The plurality of images captured by the plurality of cameras may be compared to detect a boundary suitable for synthesis. The plurality of images may be synthesized at the boundary.

Abstract: A mobile device runs a tolling application program that, in part, can perform several vehicle occupant detection processes in order to verify the number of occupants in a vehicle. When the number of occupants is verified, then (when the number is more than one) discounts on tolls can be applied to toll transactions. In order to avoid the effect of false positive detections, several occupant sensing modalities are used and the results of each are evaluated to ensure that the correct number of occupants has been determined.

Abstract: An object tracking system that includes a plurality of sensors and a tracking system. A first sensor from the plurality of sensors is configured to capture a first frame of a global plane for at least a portion of the space. The tracking system is configured to determine a pixel location in the first frame for an object located in the space, and to apply a homography to the pixel location to determine a coordinate in the global plane. The homography is configured to translate between pixel locations in the first frame and coordinates in the global plane.

Abstract: This disclosure relates to improved techniques for performing computer vision functions, including common object detection and instance segmentation. The techniques described herein utilize neural network architectures to perform these functions in various types of images, such as natural images, UAV images, satellite images, and other images. The neural network architecture can include a dense location regression network that performs object localization and segmentation functions, at least in part, by generating offset information for multiple sub-regions of candidate object proposals, and utilizing this dense offset information to derive final predictions for locations of target objects. The neural network architecture also can include a discriminative region-of-interest (RoI) pooling network that performs classification of the localized objects, at least in part, by sampling various sub-regions of candidate proposals and performing adaptive weighting to obtain discriminative features.

Abstract: Embodiments of the present invention provide a system for executing multiple events in response to receiving a video and extracting identity and contact information from that video. A video showing multiple individuals associated with the multiple events is analyzed by splitting the video into individual frames and identifying unique faces in the video. A facial recognition process and an image hashing process are then applied to generate image hashes for each individual. These image hashes are then compared to known, stored image hashes to determine an identity and contact information for each individual. Once this information is collected, the system executes the multiple events based on the determined information about each individual.

Abstract: The present disclosure describes a method, device, and non-transitory computer readable storage medium for classifying an input image. The method includes extracting, by a device, a global feature of a input image. The method includes determining, by the device, a local critical area of the input image based on the input image and the global feature; and extracting, by the device, a local feature of the local critical area. The method includes obtaining, by the device, a correlation feature of the input image based on the global feature of the input image and the local feature of the local critical area of the input image. The method includes classifying, by the device, the input image to obtain a classification result based on the global feature of the input image, the local feature of the local critical area, and the correlation feature of the input image.

Abstract: The present disclosure provides a system and method for image generation. The method may include obtaining a first image of a first modality including a complete representation of a subject, obtaining a second image of a second modality including a partial representation of the subject, obtaining a trained machine learning model, generating an synthesized second image including a complete representation of the subject using the trained machine learning model based on the first image and the second image.

Abstract: The present disclosure provides a face identification method and a terminal device using the same. The method includes: obtaining a to-be-detected image; performing a brightness enhancement process on the to-be-detected image based on a preset second calculation method to generate a to-be-identified face image; obtaining a first channel value of each channel corresponding to each pixel in the to-be-identified face image; performing another brightness enhancement process on the to-be-identified face image based on each first channel value and a preset first calculation method to obtain a target to-be-identified face image; and performing a face identification process on the target to-be-identified face image to obtain an identification result. Through the above-mentioned scheme, an enhanced face identification manner for the images of low brightness is provided.

Abstract: Methods, systems, and computer readable media for recognizing one or more hand gestures of a hand-based signal or conversation are described. Some implementations include obtaining one or more unprocessed images of the hand-based signal or conversation including images of at least one of the one or more hand gestures extracting one or more spectral features from the one or more unprocessed images using a Gabor filter bank, receiving the one or more unprocessed images of the one or more hand gestures and the extracted one or more spectral features by a Convolution Neural Network (CNN), and outputting a classification for the at least one of the one or more hand gestures using the Convolution Neural Network (CNN). In some implementations, at least one of the extracted one or more spectral features and at least one of the one or more unprocessed images of the one or more hand gestures are concatenated and input to the Convolution Neural Network (CNN).

Abstract: Using various embodiments, methods and systems to provide safeguard a neighborhood or community are described. In one embodiment, a system is configured to receiving a license plate data of a vehicle, fetch indices of a set of known license plate data from a schema object, where the set includes vehicle license plate information of a plurality of vehicles of a neighborhood or community. In one embodiment, the set can be represented in memory using by a binary tree in memory. The system is then configured to sort the binary tree using a tree sorting algorithm. Thereafter, a tree searching algorithm is used to determine whether the license plate data is found in the binary tree. When the license plate data is found in the binary tree, it is determined that the vehicle is known to the neighborhood or community, otherwise it is determined to be not known.

Abstract: An optical network includes a transmitter portion configured to (i) precode an input digitized stream of symbols into a precoded symbol stream, (ii) pulse shape the precoded symbol stream with an eigenvalue channel matrix, and (iii) transmit the pulse shaped symbol stream over a digital optical link. The optical network further includes a receiver portion configured to (i) recover the pulse shaped symbol stream from the digital optical link, (ii) decompose eigenvalues of the eigenvalue channel matrix from the recovered symbol stream, and (iii) decode the decomposed symbol stream into an output symbol stream.

Abstract: Systems and methods of detecting and tracking one or more vehicles in a field of view of an imaging system using neural network processing. An electronic controller receives an input image from a camera mounted on the host vehicle. The electronic controller applies a neural network configured to output a definition of a three-dimensional bounding box based at least in part on the input image. The three-dimensional bounding box indicates a size and a position of a detected vehicle in a field of view of the input image. The three-dimensional bounding box includes a first quadrilateral shape outlining a rear or front of the detected vehicle and a second quadrilateral shape outline a side of the detected vehicle.

Abstract: An occupant modeling device includes: an acquisition section acquiring an image by imaging a region where there is a probability that a face of an occupant is present; a model fitting section generating a model of the face based on a first image acquired by the acquisition section; a tracking section adapting the model to a second image acquired after the first image; a determination section determining correctness of a facial part position included in the second image to which the model is adapted, by using learned information obtained through learning based on correct information and incorrect information regarding the facial part position; and a processing section determining whether a process in the tracking section is to be continuously executed or a process in the model fitting section is to be executed again according to a determination result in the determination section.

Abstract: A system and method for predicting pedestrian intent is provided. A prediction circuit comprising a plurality of gated recurrent units (GRUB) receives a sequence of images captured by a camera. The prediction circuit parses each frame of the sequence of images to identify one or more pedestrians and one or more objects. Using the parsed data, the prediction circuit generates a pedestrian-centric spatiotemporal graph, the parsed data comprising one or more identified pedestrians and one or more identified object. The prediction circuit uses the pedestrian-centric graph to determine a probability of one or more pedestrians crossing a street for each frame of the sequence of images.

Abstract: Disclosed herein are arrangements that facilitate the transfer of knowledge from models for a source data-processing domain to models for a target data-processing domain. A convolutional neural network space for a source domain is factored into a first classification space and a first reconstruction space. The first classification space stores class information and the first reconstruction space stores domain-specific information. A convolutional neural network space for a target domain is factored into a second classification space and a second reconstruction space. The second classification space stores class information and the second reconstruction space stores domain-specific information. Distribution of the first classification space and the second classification space is aligned.

Abstract: The present disclosure relates to a method for security and/or automation systems. In some embodiments, the method may include receiving a reservation request for a reservation at a building from a guest. The method may further include approving the reservation request. The method may further include automatically communicating a temporary guest code to the guest based at least in part on the approving, the temporary guest code granting access to the building and access to operate at least one component of the security and/or automation system.

Abstract: The method for address verification preferably includes: receiving an unverified address; parsing the unverified address into address elements; determining a candidate address set based on the address elements; determining an address comparison set from the verified address database; selecting an intended address from the address comparison set; optionally facilitating use of the intended address; and optionally determining and providing a call to action based on the intended address.

Abstract: Disclosed is an apparatus for passenger recognition and boarding/alighting support of an autonomous vehicle. An apparatus for passenger recognition and boarding/alighting support of an autonomous vehicle according to an embodiment of the present disclosure may include a vehicle communicator configured to receive scheduled passenger information, a sensor configured to sense people outside the vehicle, and a vehicle controller configured to extract a passenger candidate group by analyzing the sensed people outside the vehicle, and calculate the number of reserved passengers using the extracted passenger candidate group and the received scheduled passenger information. One or more of an autonomous vehicle, a server, and a terminal of the present disclosure may be associated or combined with an artificial intelligence module, a drone (Unmanned Aerial Vehicle, UAV), a robot, an AR (Augmented Reality) device, a VR (Virtual Reality) device, a device associated with 5G network services, etc.

Abstract: The present application discloses a method and a system for person re-identification, the method including: inputting a training set to a model-to-be-trained, and determining a single-class label and memory features of each image data in the training set; determining multi-class labels through positive label prediction according to the single-class labels and a memory feature set; determining classification scores according to image features of each image data in the training set and the memory feature set; determining a multi-label classification loss according to the multi-class labels and the classification scores; and updating and training the model-to-be-trained to obtain a re-identification model according to the multi-label classification loss.

Abstract: In a general aspect, MIMO transmissions are elicited from wireless communication devices for wireless sensing. A first wireless communication device may be configured to generate network or transport layer messages addressed to a second wireless communication device in a wireless communication network, and wirelessly transmit the network or transport layer messages to the second wireless communication device to elicit MIMO transmissions from the second wireless communication device. The first wireless communication device may further be configured to receive MIMO transmissions from the second wireless communication device, where the MIMO transmissions traverse a space between the first wireless communication device and the second wireless communication device.