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Hand raising detection method based on deep learning

A detection method and deep learning technology, applied in the field of hand raising detection based on deep learning, can solve the problems of low accuracy and recall rate, unrobust detection results, poor Haar feature raised hand detection effect, etc. High rate, the effect of enhancing the effect

Active Publication Date: 2018-03-16
SHANGHAI JIAO TONG UNIV
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Problems solved by technology

[0005] Although the above method can obtain detection results, there are still some shortcomings: (1) Face detection is required, and the effect of face detection will directly affect the final effect of hand-raised detection; (2) The selection of the region of interest needs to be continuously Try to make a new selection plan for the new detection environment, and the detection result is not robust; (3) The hand-raising detection effect based on Haar features is not good, and the accuracy and recall rate are low

Method used

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  • Hand raising detection method based on deep learning
  • Hand raising detection method based on deep learning
  • Hand raising detection method based on deep learning

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Embodiment 1

[0088] In this embodiment, the above method is described by taking the classroom environment of primary and middle school students as an example. Collect 40,000 samples and make hand samples in the format of the PASCALVOC dataset. Through the clustering of the sample size, the final clustered 9 anchor box sizes are:

[0089] (37,59)(44,72)(53,80)(56,96)(67,105)(75,128)(91,150)(115,184)(177,283).

[0090] The training process in this embodiment has been iterated a total of 20,000 times, and a hand-raising detection model with better effect is obtained. Some renderings of the trained hand-raising detection model are as follows: Figure 7 shown.

[0091] After using the tracking algorithm to combine different frames of hand-raising movements, count the number, record the number of hand-raising movements in the entire classroom, and complete the count of hand-raising movements in a classroom, so as to evaluate the classroom atmosphere and provide a basis for the classroom atmos...

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Abstract

The invention relates to a hand raising detection method based on deep learning. The method comprises the steps that (1) samples are collected, wherein the samples are complicated environment samples;(2) a hand raising detection model is established, wherein the hand raising detection model is based on a convolutional neural network structure and is trained through an R-FCN target detection algorithm based on the samples; and (3) the trained hand raising detection model is utilized to perform hand raising detection on a to-be-detected video, and a hand raising box position is obtained. Compared with the prior art, the method has the advantages that a hand raising action in a complicated environment can be detected, and the accuracy rate and the recall rate are high.

Description

technical field [0001] The present invention relates to a video detection method, in particular to a hand-raising detection method based on deep learning. Background technique [0002] Human body detection and behavior recognition in video sequences is a research topic involving computer vision, pattern recognition and artificial intelligence. hot spot. However, it is still difficult to come up with a robust and real-time accurate method because of the diversity and non-rigidity of human behaviors and the inherent complexity of video images. [0003] Detecting human hand-raising motions in a typical classroom environment is a challenging task due to noisy and highly dynamic backgrounds, varying lighting conditions, as well as small size and multiple possible matching objects. [0004] The document "Haar-Feature Based Gesture Detection of Hand-Raising for MobileRobot in HRI Environments" discloses a hand-raising detection technology based on Haar features. This method first...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/254G06K9/00G06K9/62
CPCG06T7/0002G06T7/254G06T2207/10016G06T2207/20081G06T2207/20084G06T2207/30232G06T2207/30242G06V20/46G06V20/52G06F18/23G06F18/24147
Inventor 林娇娇姜飞申瑞民
Owner SHANGHAI JIAO TONG UNIV
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