Video behavior identification method and system

A recognition method and behavior technology, applied in character and pattern recognition, instruments, biological neural network models, etc., can solve the problems of affecting recognition accuracy, not fully considering the timing information and context information of a single person, etc.

Pending Publication Date: 2020-12-25
SHENZHEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Therefore, the technical problem to be solved by the present invention is to overcome the defect that the video behavior recognition method in the prior art does not fully c

Method used

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  • Video behavior identification method and system
  • Video behavior identification method and system

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Experimental program
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Embodiment 1

[0067] The embodiment of the present invention provides a video behavior recognition method, which can be applied to various scenarios such as video behavior recognition. A typical application scenario is sports video understanding, automatic analysis of sports tactics, etc. Sports video is an important type of media data. With a large audience and huge application prospects, it has attracted extensive attention from academia and industry. With the popularization of mobile devices and the Internet, people's demand for sports videos has shifted from direct viewing and simple browsing to diversified needs, such as highlight summary, specific event detection, program customization services, video content editing, etc., all of which rely on sports video understanding and behavior recognition. In sports games such as baseball, football, tennis, and volleyball, behavior recognition includes not only a single person performing a series of actions to complete a certain task, that is, ...

Embodiment 2

[0110] An embodiment of the present invention provides a video behavior recognition system, such as Figure 7 shown, including:

[0111] The feature extraction module 10 is used for performing multi-level feature extraction of the video to be identified. This module executes the method described in step S10 in Embodiment 1, which will not be repeated here.

[0112] The ROI initial detection module 20 is configured to perform initial detection of the ROI of the target object by using a deep fully convolutional network. This module executes the method described in step S20 in Embodiment 1, which will not be repeated here.

[0113] The ROI fine-tuning module 30 is configured to fine-tune the ROI by using the Markov random field to obtain the ROI set of the final target object. This module executes the method described in step 30 in Embodiment 1, which will not be repeated here.

[0114] Behavior recognition module 40 is used to simultaneously perform single-person behavior re...

Embodiment 3

[0117] An embodiment of the present invention provides a computer device, such as Figure 8 As shown, the device may include a processor 51 and a memory 52, wherein the processor 51 and the memory 52 may be connected via a bus or in other ways, Figure 8 Take connection via bus as an example.

[0118] The processor 51 may be a central processing unit (Central Processing Unit, CPU). Processor 51 can also be other general processors, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-Programmable Gate Array, FPGA) or Other chips such as programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above-mentioned types of chips.

[0119]As a non-transitory computer-readable storage medium, the memory 52 can be used to store non-transitory software programs, non-transitory computer-exec...

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PUM

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Abstract

The invention discloses a video behavior identification method and system. The method comprises the following steps: carrying out multi-level feature extraction on a to-be-identified video; performinginitial detection on the ROI of the target object by using a deep full convolutional network; carrying out ROI fine adjustment by utilizing a Markov random field to obtain an ROI set of a final target object; and based on the ROI set of the final target object, carrying out single-person behavior identification and group behavior identification at the same time. According to the method, the consistency of time sequence information in a group is considered, meanwhile, the difference of individual time sequence information is also considered, single-person behavior recognition based on ROI timesequence reasoning is helpful for better extracting discriminative single-person behavior characteristics, and the recognition precision is improved; based on the ROI matching recurrent convolutionalnetwork, the ROI information of a single person in the time domain can be fused and propagated, and the method is an effective method for solving the problem of video behavior recognition.

Description

technical field [0001] The invention relates to the technical field of behavior recognition, in particular to a video behavior recognition method and system. Background technique [0002] In recent years, behavior recognition algorithms have developed rapidly, and group behavior recognition based on deep learning has also achieved good results. The current technology based on deep learning methods has achieved good recognition performance in group behavior recognition. However, all current studies use group videos as a whole to conduct research, while ignoring individual behavior recognition that is equally important as group behavior. Since group behavior is not a simple superposition of individual behaviors, it is a comprehensive definition of the timing information of a single individual behavior and the interaction between individuals to obtain specific group behaviors. Therefore, only group behaviors are considered to extract group characteristics, and individual behav...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/32G06K9/62G06N3/04
CPCG06V40/20G06V20/40G06V10/25G06N3/045G06F18/253
Inventor 李岩山刘燕谢维信
Owner SHENZHEN UNIV
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