Tcl Research America Inc.

Xinyao Sun

Haohong Wang

Xiaobo Ren

Xinpeng Liao

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

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 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.

Growing community of inventors

San Jose, CA, United States of America

Xinyao Sun