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Gesture recognition method and equipment

A gesture, to-be-recognized technology, applied in the field of gesture recognition to automatically adapt to changes in posture and speed up the training process

Active Publication Date: 2017-09-26
HISCENE INFORMATION TECH CO LTD
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

It can be seen that this scheme has flaws, especially when dealing with large changes in poses

Method used

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  • Gesture recognition method and equipment
  • Gesture recognition method and equipment

Examples

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Embodiment Construction

[0030] The application will be described in further detail below in conjunction with the accompanying drawings.

[0031] In a typical configuration of the present application, the terminal, the device serving the network and the trusted party all include one or more processors (CPUs), input / output interfaces, network interfaces and memory.

[0032] Memory may include non-permanent storage in computer readable media, in the form of random access memory (RAM) and / or nonvolatile memory such as read only memory (ROM) or flash RAM. Memory is an example of computer readable media.

[0033] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can be implemented by any method or technology for storage of information. Information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random acce...

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Abstract

The invention aims to provide a gesture recognition method and equipment. Compared with the prior art, the gesture recognition method comprises the following steps: based on gesture training data and corresponding bone joint tag information, performing training to obtain a plurality of random decision trees, wherein each random decision tree comprises one or more partitioning nodes and partitioning index point information corresponding to each partitioning node; acquiring deep image information of a to-be-recognized gesture; for each random decision tree, determining candidate bone joint coordinate information corresponding to the deep image information according to one or more partitioning nodes and partitioning index point information corresponding to each partitioning node; on the basis, determining bone joint coordinate information corresponding to the deep image information, and recognizing the gesture. The invention provides a novel random decision forest growth strategy, bone joint coordinate grouping and random characteristic selection are guided by using partitioning index points in a self-adaptive manner, and relatively flexible grouping strategies which can be automatically applicable to gesture variations are adopted.

Description

[0001] This case claims the priority of CN201610395477.3 technical field [0002] The present application relates to the field of computers, and in particular to a gesture recognition technology. Background technique [0003] Skeletal detection and pose estimation of an object with a complex joint structure is always a challenging topic in computer vision. For example, accurately estimating gestures or human poses plays an important role in the field of human-computer interaction. Due to the practical value associated with this topic, it has attracted input from both industry and academia. In the past few years, the application of real-time estimation of human pose has also emerged in everyday life by using low-cost, high-speed depth sensors. Since then, human pose estimation has received increasing attention. Many new algorithms have outperformed traditional RGB image-based human pose estimation algorithms due to the provision of a new type of low-cost input data - depth...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/28G06F18/214
Inventor 李佩易廖春元
Owner HISCENE INFORMATION TECH CO LTD
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