US 12,299,968 B2
Cascaded neural network-based attention detection method, computer device, and computer-readable storage medium
Xiaohui Li, Guangdong (CN); Gang Peng, Guangdong (CN); Nan Nan, Guangdong (CN); and Liping Ye, Guangdong (CN)
Assigned to ALLWINNER TECHNOLOGY CO., LTD., Guangdong (CN)
Appl. No. 17/631,083
Filed by ALLWINNER TECHNOLOGY CO., LTD., Guangdong (CN)
PCT Filed Jul. 30, 2019, PCT No. PCT/CN2019/098407
§ 371(c)(1), (2) Date Mar. 24, 2022,
PCT Pub. No. WO2021/016873, PCT Pub. Date Feb. 4, 2021.
Prior Publication US 2022/0277558 A1, Sep. 1, 2022
Int. Cl. G06V 10/00 (2022.01); G06V 10/22 (2022.01); G06V 10/82 (2022.01); G06V 20/40 (2022.01); G06V 20/59 (2022.01); G06V 40/16 (2022.01)
CPC G06V 10/82 (2022.01) [G06V 10/22 (2022.01); G06V 20/41 (2022.01); G06V 20/597 (2022.01); G06V 40/161 (2022.01)] 9 Claims
OG exemplary drawing
 
1. An attention detection method based on a cascade neural network, comprising:
obtaining video data, recognizing a plurality of image frames, and extracting a face region of the plurality of image frames;
wherein,
recognizing the face region by using a first convolutional neural network to judge whether a first situation of inattention occurs; and
recognizing, if it is confirmed that no first situation of inattention occurs, the face region by using a second convolutional neural network to judge whether a second situation of inattention occurs, wherein
computational complexity of the first convolutional neural network is less than computational complexity of the second convolutional neural network.