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Behavior recognition method, device, storage medium and processor

A recognition method and behavior technology, applied in the field of image processing, can solve the problem of low accuracy of behavior recognition, and achieve the effect of improving the accuracy

Active Publication Date: 2020-02-04
HUAZHONG UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The main purpose of the present invention is to provide a behavior recognition method, device, storage medium and processor, to at least solve the problem of low accuracy of behavior recognition

Method used

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  • Behavior recognition method, device, storage medium and processor
  • Behavior recognition method, device, storage medium and processor
  • Behavior recognition method, device, storage medium and processor

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0033] An embodiment of the present invention provides a behavior recognition method.

[0034] figure 1 is a flowchart of a behavior recognition method according to an embodiment of the present invention. Such as figure 1 As shown, the behavior recognition method includes the following steps:

[0035] Step S102, obtaining a first hypergraph model, wherein the first hypergraph model is used to characterize the correlation between multiple action sequences, each action sequence is used to indicate multiple gestures of the target object, and multiple gestures are used together for Indicates the behavior change process of the target object.

[0036] In the technical solution provided in the above step S102 of this application, the behavior recognition method is a hypergraph-based behavior recognition method, which can be used to recognize the posture of the target object, for example, to recognize the posture of the hand, which can be used to recognize dynamic gestures , for e...

Embodiment 2

[0086] In the embodiment of the present invention, the behavior recognition method of the embodiment of the present invention is tested on three existing data sets and one collected data set. The above data sets are MSRGesture3D data set, MSRAction3D data set, and MSRActionPairs data Set and GesturnMotion dataset, the test results are as follows:

[0087] The recognition rate on the MSRGesture3D dataset is 98.50%, the recognition rate on the MSRAction3D dataset is 96.70%, the recognition rate on the MSRActionPairs dataset is 99.44%, and the recognition rate on the GesturnMotion dataset is 97.14%, thus improving behavior recognition accuracy.

Embodiment 3

[0089] The embodiment of the present invention also provides a behavior recognition device. It should be noted that the behavior recognition device in this embodiment can be used to implement the behavior recognition method in the embodiment of the present invention.

[0090] figure 2 is a schematic diagram of a behavior recognition device according to an embodiment of the present invention. Such as figure 2 As shown, the device includes: an acquisition unit 10 , a construction unit 20 , a classification unit 30 and an identification unit 40 .

[0091] The acquisition unit 10 is configured to acquire a first hypergraph model, wherein the first hypergraph model is used to characterize the correlation between multiple action sequences, each action sequence is used to indicate multiple gestures of the target object, and the multiple gestures Commonly used to represent the behavior change process of the target object.

[0092] The construction unit 20 is configured to constr...

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Abstract

The invention discloses a behavior recognition method, device, storage medium and processor. The method includes: obtaining a first hypergraph model, wherein the first hypergraph model is used to characterize the correlation between multiple action sequences, each action sequence is used to indicate multiple gestures of the target object, and the multiple gestures share a common To represent the behavior change process of the target object; construct the second hypergraph model according to all the hyperedges of the first hypergraph model, wherein the second hypergraph model is used to carry out the weight of each hyperedge in the first hypergraph model processing; classifying each action sequence through the processed second hypergraph model to obtain the category of each action sequence; identifying the target behavior of the target object according to the category of each action sequence. Through the present invention, the effect of improving the accuracy of behavior recognition is achieved.

Description

technical field [0001] The present invention relates to the field of image processing, in particular to a behavior recognition method, device, storage medium and processor. Background technique [0002] At present, in the behavior recognition method, fixed features are extracted through the movement trajectory of the action, and then the fixed features are matched with the existing feature model to obtain the action recognition result. [0003] A behavior recognition method is also provided in the related art, which extracts one or several features for the action sequences used for training and testing, and then performs fusion processing and dimension reduction processing on one or several features to obtain the feature vector , and then train the existing feature vectors to obtain a classification model, and finally classify according to the action sequence to be tested by the classification model. [0004] The above behavior recognition method does not use the hypergraph...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/20G06F18/24
Inventor 刘琼程驰杨铀
Owner HUAZHONG UNIV OF SCI & TECH