Midea Group Co., Ltd.

Gd Midea Air-conditioning Equipment Co., Ltd.

Zhicai Ou

Thanh Huy Ha

Yunke Tian

Yi Heng Chen

Xin Chen

Suresh Mani

Weiying Li

Hua Zhou

Mohamad Al Jazaery

Yuxiang Gao

Yi Fan

Rubao Ma

Sathyanarayanan Muthusamy

A method and system for capturing, by a camera a sequence of frames at respective locations within a portion of an environment; capturing, by an inertial measurement unit, a sequence of inertial odometry data corresponding to the sequence of frames at the respective locations; storing in a queue a data record includes information extracted from processing the respective frame and information from the inertial measurement unit; in accordance with a determination that the sequence of inertial odometry data satisfies a first criterion: calculating a first relative pose between the first frame and the second frame; and in accordance with a determination that a difference between the first relative pose and the information extracted from processing the respective frame satisfy a first threshold: generating an initial map of the portion of the environment based on the first data record and the second data record.

Fast and robust initialization method for feature-based monocular visual SLAM using inertial odometry assistance

System and method for reducing false negatives in object detection, including: extracting an object of interest from a respective image in a first set of training data that includes in distribution (ID) data. For each of the object of interest extracted from the respective image in the first set of training data: fusing the object of interest with an image from a second set of data that does not include any objects of interest to form a fused image; adding the fused image to the training data; and using the training data to train a detection model for object detection.

Method and system for reducing false positives in object detection neural networks caused by novel objects

The present disclosure provides a method for controlling a terminal, including the following operations: obtaining recognition results corresponding to control signals after receiving the control signals, and determining whether control instructions corresponding to the recognition results conflict, each control signal comprising at least one of a voice signal or a gesture signal; determining a credibility of each control instruction in response to a determination that there exists conflict among control instructions; and sending the control instruction with highest credibility to a control terminal. The present disclosure further provides a device for controlling a terminal and a computer readable storage medium. When control instructions are received and there exists conflict among control instructions, the control instruction with the highest credibility is sent to the control terminal after the credibility of each control instructions is determined, thereby avoiding settings from conflict.

Method and device for controlling terminal, and computer readable storage medium

An air conditioning system includes one or more cameras, one or more air conditioning operation units, and an air conditioner control unit that obtains, via the one or more cameras, images of a surrounding environment of the air conditioning operation units, and determines a first set of factors, including a layout of the surrounding environment, based on analysis of the images through predefined machine learning models. The control unit, in accordance with the first set of factors that has been determined based on the analysis of the images through the predefined machine learning models, selects a first operation profile from a plurality of predefined operation profiles to control the air conditioning operation units.

Method and system for providing air conditioning

System and method for controlling a machine, including: receiving a first image processing model trained to classify an input image into a first class for images containing at least one object of a first type or a second class for images not containing an object of the first type; identifying a subset of inference results that are false positive results; generating a set of new training data from the first set of images, including augmenting an image in the first set of images to obtain a respective plurality of images and labeling the respective plurality of images as containing at least one object of a pseudo first class; training a second image processing model to classify an input image into the first class, the second class, and the first pseudo class; and modifying a device setting of a machine based on an inference result of the second image processing model.

Adjusting machine settings through multi-pass training of object detection models

A method includes: capturing one or more images of an unorganized collection of items inside a first machine; determining one or more item types of the unorganized collection of items from the one or more images, comprising: dividing a respective image in the one or more images into a respective plurality of sub-regions; performing feature detection on the respective plurality of sub-regions to obtain a respective plurality of regional feature vectors, wherein a regional feature vector for a sub-region indicates characteristics for a plurality of predefined local item features for the sub-region; generating an integrated feature vector by combining the respective plurality of regional feature vectors; and applying a plurality of binary classifiers to the integrated feature vector; and selecting a machine setting for the first machine based on the determined one or more clothes type in the unorganized collection of items.

Method and system for controlling machines based on object recognition

A method and system of providing a control user interface at a first home appliance are disclosed, the method including detecting presence of a user within a threshold range of the first home appliance, and performing image processing on one or more real-time images of the user to determine one or more characteristics of a facial image of the user; determining at least a first parameter that is configured to trigger a first change in a current control user interface configuration for the first home appliance; and activating a first control user interface configuration corresponding to the first parameter for the first home appliance while the presence of the first user continues to be detected within the threshold range of the first home appliance.

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San Jose, CA, United States of America

Zhicai Ou