Tcl Research America Inc.

Xiaobo Ren

Haohong Wang

Xinpeng Liao

Guanghan Ning

Xinyao Sun

Wenqiang Bo

Jianbo Yuan

Zhi Zhang

Lifan Guo

Yimin Yang

Qiqin Dai

An experience-aware anomaly processing system and a method for an experience-aware anomaly processing system are provided. The experience-aware anomaly processing system comprises an anomaly detection module configured to receive geographic location data with corresponding time information of a target object, and analyze target object behavior based on the geographic location data with corresponding time information of the target object; a user feedback module configured to receive user feedback from a user and model user feedback behavior when the user receives an alarm message indicating the target object is abnormal; and a decision module configured to receive user setting from the user, and make a detection decision through fusing target object behavior information corresponding to the target object behavior, user feedback behavior information corresponding to the user feedback behavior, and the user setting.

Experience-aware anomaly processing system and method

A method for optimized wake-up strategy via sleeping stage prediction with recurrent neural networks is provided. The method comprises receiving a set of raw sensory data of a user's sleeping status; translating the set of raw sensory data into a plurality of sleeping stages; saving the plurality of sleeping stages as a history of sleeping stages; determining a current sleeping stage; predicting a plurality of future sleeping stages of the user in an upcoming interval; calculating a wakeup score of the current sleeping stage and a wakeup score of each future sleeping stage; determining an optimized sleeping stage for wake up based on the wakeup score through comparing the wakeup score of the current sleeping stage and the wakeup scores of the future sleeping stages; and triggering an alarm impulse to wake up the user at the optimized sleeping stage for wake up.

Method and system for optimized wake-up strategy via sleeping stage prediction with recurrent neural networks

The present disclosure provides a method and device for visual appearance based person identity inference. The method may include obtaining a plurality of input images. The input images include a gallery set of images containing, persons-of-interest and a probe set of images containing person detections, and one input image corresponds to one person. The method may further include extracting N feature maps from the input images using a Deep Neural Network, N being a natural number; constructing N structure samples of the N feature maps using conditional random field (CRF) graphical models; learning the N structure samples from an implicit common latent feature space embedded in the N structure samples; and according to the learned structures, identifying one or more images from the probe set containing a same person-of-interest as an image in the gallery set.

Method and device for Quasi-Gibbs structure sampling by deep permutation for person identity inference

A system and a method for vision-based self-stabilization by deep gated recurrent Q-networks (DGRQNs) for unmanned arial vehicles (UAVs) are provided. The method comprises receiving a plurality of raw images captured by a camera installed on a UAV; receiving an initial reference image for stabilization and obtaining an initial camera pose from the initial reference image; extracting a fundamental matrix between consecutive images and estimating a current camera pose relative to the initial camera pose, wherein the camera pose includes an orientation and a location of the camera; based on the estimated current camera pose, predicting an action to counteract a lateral disturbance of the UAV based on the DGRQNs; and based on the predicted action to counteract the lateral disturbance of the UAV, driving the UAV back to the initial camera pose.

System and method for vision-based flight self-stabilization by deep gated recurrent Q-networks

In accordance with some embodiments of the disclosed subject matter, a method and a system for recommending and sharing customized multimedia route planning are provided. The method includes: receiving a query image from a user, the query image containing an object-of-interest of the user, performing an integrative segmentation process to determine one or more contours of the object-of-interest in the query image; determining a route having a maximum area overlap with the one or more contours of object-of-interest on a map image; generating an output image including the object-of-interest and the route; and recommending the output image to the user, and sharing the output image on a social network platform.

Method and system for sharing-oriented personalized route planning via a customizable multimedia approach

A system and a method for enhancing target tracking via detector and tracker fusion for unmanned aerial vehicles (UAVs) are provided. The method comprises receiving at least one raw input image of objects to be detected; based on the at least one raw input image of objects, generating the objects' candidate information; based on the objects' candidate information, calculating location and velocity estimation of an object at a current timestamp based on a detector and tracker fusion; and based on the location and velocity estimation of the object at the current timestamp, predicting the location and velocity estimation of the object at a future timestamp.

System and method for enhancing target tracking via detector and tracker fusion for unmanned aerial vehicles

A method for personalized intelligent wake-up system based on multimodal deep neural network comprises monitoring a sleeping status of a user; obtaining a current sleeping-stage of the user within a current time frame and a prediction of a next sleeping-stage of the user for a next time frame; correcting the current sleeping-stage of the user through combining the current sleeping-stage and the prediction of the next sleeping-stage; determining a wake up strategy for the current time frame; determining a relationship between each of a plurality of alarm impulses adopted to wake up the user and a corresponding reaction of the user; identifying a change in the current sleeping-stage for the current time frame; determining an alarm impulse to be triggered for waking up the user; and triggering the determined alarm impulse.

Personalized intelligent wake-up system and method based on multimodal deep neural network

A face detection and tracking method of a robotic device. The method includes obtaining a video frame from a camera of the robotic device; performing a face detection process on the video frame to detect one or more faces in the video frame and, after the face detection process, identifying the detected one or more faces in the video frame. The method also includes performing a vision-based tracking process to track the identified one or more faces using a combination of a feature points tracker and a correlation tracker and, after the vision-based tracking process, performing a detection and tracking fusion process and providing desired target prediction of the identified one or more faces.

Face detection, identification, and tracking system for robotic devices

The present invention discloses a method for recommending an image-based route. The method includes: obtaining a query image from a user; performing an interactive segmentation process to determine one or more contours of an object-of-interest in the query image; determining a route having the maximum area overlap with the one or more contours of the object-of-interest on a map image; generating an output image including the object-of-interest and the route having the maximum area overlap with the contours of the object-of-interest; and recommending the output image to the user, and sharing the output image on a social network platform.

Method and system for building personalized knowledge base of semantic image segmentation via a selective random field approach

A method for a scalable storage-loss optimized (SSLO) system for color gamut mapping is provided. The SSLO system method comprises generating a pre-computed color gamut mapping between an input color gamut and an output color gamut, receiving an input signal including a plurality of input pixels from the input signal domain, wherein the input pixels correspond to a set of first color values represented in a first color space; converting the set of the first color values to be a set of second color values represented in a second color space; generating a set of third color values through a mapping modeling and a color reproduction; converting the set of the third color values to be a set of fourth color values represented in the first color space; and outputting an output signal including a plurality of output pixels corresponding to the fourth color values to the output signal domain.

Scalable storage-loss optimized framework based method and system for color gamut mapping

The present disclosure provides a method for a privacy-aware indoor drone exploration and communication framework, including: receiving a communication request for a target person from an outside caller; locating the target person in response to the communication request; establishing a video call between the target person and the outside caller to transmit images of the target person to the outside caller; and avoiding showing privacy areas and privacy objects in the indoor environment in the images of the target person to the outside caller during the video call.

Privacy-aware indoor drone exploration and communication framework

A content retrieval method including: extracting a plurality of fingerprints including a plurality of video fingerprints and audio fingerprints from contents stored in a content database; determining representative video fingerprints of the video frames and representative audio fingerprints of the audio sequences; determining a data rate indicating a storage limitation and a coverage indicating a number of searching results to be returned; storing selected representative video fingerprints and representative audio fingerprints based on the storage limitation indicated by the data rate in a fingerprint database; receiving a query containing at least one of video data and audio data submitted by a user; extracting at least one query fingerprint representing the query; determining a number of fingerprints most matching the query fingerprint based on the coverage to generate search results indicating matching contents represented by the number of most matching fingerprints; and returning the search results to the user.

Method and system for content retrieval based on rate-coverage optimization

A method for a mobile search-ready smart display system containing a smart display having a screen and at least one mobile device having a camera including extracting a plurality of video fingerprints from an ongoing content displayed on the screen. The ongoing content has video frames corresponding to the video fingerprints; determining one or more representative video fingerprints of the video frames from the plurality of video fingerprints; generating one or more encoded representative video fingerprints through encoding the one or more representative video fingerprints into a pixel translucency change; and delivering the one or more encoded representative video fingerprints via a screen-camera communication. The method also includes optimizing the encoding of the one or more representative video fingerprints and the delivering of the one or more encoded representative video fingerprints to find a balance between an error resilience and transmission time.

Mobile search-ready smart display technology utilizing optimized content fingerprint coding and delivery

In accordance with various embodiments of the disclosed subject matter, a method and a system for vision-centric deep-learning-based road situation analysis are provided. The method can include: receiving real-time traffic environment visual input from a camera; determining, using a ROLO engine, at least one initial region of interest from the real-time traffic environment visual input by using a CNN training method; verifying the at least one initial region of interest to determine if a detected object in the at least one initial region of interest is a candidate object to be tracked; using LSTMs to track the detected object based on the real-time traffic environment visual input, and predicting a future status of the detected object by using the CNN training method; and determining if a warning signal is to be presented to a driver of a vehicle based on the predicted future status of the detected object.

Method and system for vision-centric deep-learning-based road situation analysis

The present invention provides a method for converting a low dynamic range (LDR) image to a high dynamic range (HDR) image, including obtaining an input LDR image; and in an HDR image database, selecting one or more HDR images that match the input LDR image as candidate images. Further, the candidate images are warped according to a spatial correspondence between the candidate images and the input LDR image. The input LDR image is decomposed to an illumination component and a texture component. The illumination component and the texture component are reconstructed respectively based on the warped candidate images. The reconstructed illumination component and the reconstructed texture component are combined to produce an output HDR image.

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San Jose, CA, United States of America

Xiaobo Ren