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Wei Hua

from Mountain View, CA
Age ~43
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Also known as:
Hua Wei
Related to:
Wei Wang, 57Li Xue, 49Wei Peng, 55Kevin Li, 31Wei LiuLi WeiTiantian Li, 41Wei Li, 50Kexin LiWei Li, 46Li Liang, 59Xiaozhong LiWeitao Li, 46Dongyi Lin, 44Chao LiMaggie W Lee, 51Wei Li, 54
Connected to:
Anna Hua, 44Audrey E Hua, 36Christina W Hua, 40Chuong K Hua, 60Dong Hua, 71Elaine D Hua, 32Fa F Hua, 62Guichao Hua, 59Hannah Hua, 46Heng Hua, 72

Wei Hua Phones & Addresses

  • Mountain View, CA
  • San Francisco, CA
  • Foster City, CA
  • Arlington, TX
  • Indianapolis, IN
  • Waco, TX
  • Frisco, TX

Resumes

Resumes

Wei Hua Photo 1

Technician Lead And Engineering Manager

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Location:
1495 Kings Ln, Palo Alto, CA 94303
Industry:
Computer Software
Work:
Google
Technician Lead and Engineering Manager
Vidient Jun 2004 - Nov 2010
Co-Founder and Principal Video Scientist
Nec Laboratories America, Inc. Jan 2001 - Jul 2004
Researcher
Carnegie Mellon University May 1998 - Dec 2001
Research Engineer
Education:
University of Pittsburgh 1996 - 1998
Masters, Computer Science Tsinghua University 1987 - 1994
Masters, Bachelors, Computer Science
Skills:
Computer Vision
Machine Learning
Pattern Recognition
Algorithms
Video Processing
C++
Software Engineering
Android
Distributed Systems
Python
Scalability
Video Analytics
Cloud Computing
Artificial Intelligence
Languages:
English
Wei Hua Photo 2

Wei Hua

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Wei Hua Photo 3

Wei Hua

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Wei Hua Photo 4

Wei Hua

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Wei Hua Photo 5

Wei Hua

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Location:
San Francisco Bay Area
Industry:
Computer Software
Wei Hua Photo 6

Stockbroker At Scottrade

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Location:
San Francisco Bay Area
Industry:
Financial Services

Business Records

Name / Title
Company / Classification
Phones & Addresses
Wei Li Hua
President
HUA WAVE, INC
10138 Colina Ave, San Ramon, CA 94583
10138 Colima Ave, San Ramon, CA 94583

Publications

Us Patents

Method Of And System For Hierarchical Human/Crowd Behavior Detection

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US Patent:
8195598, Jun 5, 2012
Filed:
Nov 17, 2008
Appl. No.:
12/313193
Inventors:
Wei Hua - Cupertino CA, US
Xiangrong Chen - Fremont CA, US
Ryan Crabb - Los Gatos CA, US
Juwei Lu - San Jose CA, US
Jonathan Cook - Los Gatos CA, US
Assignee:
Agilence, Inc. - Camden NJ
International Classification:
G06F 15/00
G06F 15/18
US Classification:
706 62, 706 45, 706 47, 382100, 382103, 382107
Abstract:
The present invention is directed to a computer automated method of selectively identifying a user specified behavior of a crowd. The method comprises receiving video data but can also include audio data and sensor data. The video data contains images a crowd. The video data is processed to extract hierarchical human and crowd features. The detected crowd features are processed to detect a selectable crowd behavior. The selected crowd behavior detected is specified by a configurable behavior rule. Human detection is provided by a hybrid human detector algorithm which can include Adaboost or convolutional neural network. Crowd features are detected using textual analysis techniques. The configurable crowd behavior for detection can be defined by crowd behavioral language.

Native Machine Learning Service For User Adaptation On A Mobile Platform

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US Patent:
8429103, Apr 23, 2013
Filed:
Aug 2, 2012
Appl. No.:
13/565508
Inventors:
Hrishikesh Aradhye - Santa Clara CA, US
Wei Hua - Palo Alto CA, US
Assignee:
Google Inc. - Mountain View CA
International Classification:
G06F 15/18
G09B 19/00
US Classification:
706 12
Abstract:
Disclosed are apparatus and methods for providing machine-learning services. A machine-learning service executing on a mobile platform can receive data related to a plurality of features. In some cases, the received data can include data related to features received from an application and data related to features received from the mobile platform. The machine-learning service can determine at least one feature based on the received data. The machine-learning service can generate an output by performing a machine-learning operation on the at least one feature. The machine-learning operation can be selected from among an operation of ranking the at least one feature, an operation of classifying the at least one feature, an operation of predicting the at least one feature, and an operation of clustering the at least one feature. The machine-learning service can send the output.

Active And Adaptive Intelligent Video Surveillance System

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US Patent:
8649594, Feb 11, 2014
Filed:
Jun 3, 2010
Appl. No.:
12/802265
Inventors:
Wei Hua - Palo Alto CA, US
Juwei Lu - San Jose CA, US
Jinman Kang - San Jose CA, US
Jon Cook - Los Gatos CA, US
Haisong Gu - Cupertino CA, US
Assignee:
Agilence, Inc. - Mount Laurel NJ
International Classification:
G06K 9/62
US Classification:
382159, 348143, 348159, 382155, 382190, 706 14
Abstract:
A method for assessing events detected by a surveillance system includes assessing the likelihood that the events correspond to events being monitored from feedback in response to a condition set by a user. Classifiers are created for the events from the feedback. The classifiers are applied to allow the surveillance system improve its accuracy when processing new video data.

Method To Predict A Communicative Action That Is Most Likely To Be Executed Given A Context

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US Patent:
20130346347, Dec 26, 2013
Filed:
Aug 7, 2012
Appl. No.:
13/568957
Inventors:
Anna Patterson - Saratoga CA, US
Hrishikesh Aradhye - Santa Clara CA, US
Wei Hua - Palo Alto CA, US
Daniel Lehmann - San Francisco CA, US
Assignee:
GOOGLE INC. - Mountain View CA
International Classification:
G06F 15/18
US Classification:
706 12
Abstract:
Disclosed are apparatus and methods for providing machine-learning services. A context-identification system executing on a mobile platform can receive data comprising context-related data associated with the mobile platform and application-related data received from the mobile platform. The context-identification system can identify a context using the context-related data associated with the mobile platform and/or the application-related data received from the mobile platform. Based on at least one context identified, context-identification system can predict a communicative action associated with the mobile platform by performing a machine-learning operation on the received data. An instruction can be received to execute the communicative action associated with the mobile platform.

Intuitive Inter-Device Connectivity For Data Sharing And Collaborative Resource Usage

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US Patent:
20190037611, Jan 31, 2019
Filed:
Dec 23, 2013
Appl. No.:
14/139376
Inventors:
- Mountain view CA, US
Wei HUA - Palo Alto CA, US
International Classification:
H04W 76/02
G06F 3/0486
Abstract:
Embodiments of the invention provide client devices, servers, systems, and methods for establishing, managing, and utilizing inter-device connectivity environments that enable inter-device data and resource sharing through intuitive user commands. The invention does not require client devices having specialized hardware necessary for forming direct client connections, such as NFC readers or RFID antennas. Embodiments of the invention utilize an environment wherein multiple client devices communicate with a server to enable the transfer of files from one client device to another. The server manages the transfer of data between clients and the sharing of client resources between clients. Specifically, the server determines whether inter-device sharing events detected and reported by client devices are valid inter-device sharing events. If such events are valid, the server further facilitates the data and resource sharing process.

Systems And Methods For Prioritizing Notifications On Mobile Devices

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US Patent:
20180032529, Feb 1, 2018
Filed:
Oct 6, 2017
Appl. No.:
15/727088
Inventors:
- Mountain View CA, US
Wei Hua - Palo Alto CA, US
Mohammad Saberian - La Jolla CA, US
Assignee:
Google LLC - Mountain View CA
International Classification:
G06F 17/30
G06Q 10/10
H04L 12/58
Abstract:
Certain embodiments of the disclosed technology include systems and methods for determining the priority of a notification on a mobile device using machine learning. Other aspects of the disclosed technology include selectively displaying or emphasizing notifications based on the priority of a notification.

Systems And Methods For Categorizing Motion Events

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US Patent:
20170270365, Sep 21, 2017
Filed:
Jun 2, 2017
Appl. No.:
15/613013
Inventors:
- Mountain View CA, US
Wei Hua - Palo Alto CA, US
Prateek Reddy - San Francisco CA, US
Akshay R. Bapat - Mountain View CA, US
Lawrence W. Neal - Oakland CA, US
International Classification:
G06K 9/00
H04N 7/18
H04N 5/14
G06F 3/0488
G06F 3/0481
G06F 3/0482
G06F 3/0484
G06F 3/0485
G06K 9/32
G11B 27/00
G11B 27/028
G11B 27/031
G11B 27/10
G11B 27/30
G11B 27/34
H04N 5/93
G08B 13/196
G06K 9/62
Abstract:
The various embodiments described herein include methods, devices, and systems for categorizing motion events. In one aspect, a method is performed at a camera device. The method includes: (1) capturing a plurality of video frames via the image sensor, the plurality of video frames corresponding to a scene in a field of view of the camera; (2) sending the video frames to the remote server system in real-time; (3) while sending the video frames to the remote server system in real-time: (a) determining that motion has occurred within the scene; (b) in response to determining that motion has occurred within the scene, characterizing the motion as a motion event; and (c) generating motion event metadata for the motion event; and (4) sending the generated motion event metadata to the remote server system concurrently with the video frames.

Systems And Methods For Categorizing Motion Events

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US Patent:
20170046574, Feb 16, 2017
Filed:
Oct 25, 2016
Appl. No.:
15/334172
Inventors:
- Mountain View CA, US
Wei Hua - Palo Alto CA, US
Prateek Reddy - San Francisco CA, US
Akshay R. Bapat - Mountain View CA, US
Lawrence W. Neal - Oakland CA, US
International Classification:
G06K 9/00
G06K 9/32
H04N 7/18
G08B 13/196
G06T 7/20
Abstract:
The various embodiments described herein include methods, devices, and systems for categorizing motion events. In one aspect, a method includes: (1) obtaining a plurality of video frames, the plurality of video frames corresponding to a scene and a motion event candidate; (2) identifying one or more visual characteristics of the scene; (3) obtaining one or more background factors for the scene; (4) utilizing the obtained background factors to identify one or more motion entities; (5) for each identified motion entity: (a) classifying the motion entity by performing object recognition; and (b) obtaining one or more representative motion vectors based on a motion track of the motion entity; and (6) assigning a motion event category to the motion event candidate based on the identified visual characteristics, the obtained background factors, the classified motion entities, and the obtained representative motion vectors.
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Wei Hua from Mountain View, CA, age ~43 Get Report