Human posture detection utilizing posture reference maps

A device, comprising:

• at least one processor;
• and
• a memory configured to store at least one program,
• wherein the at least one processor, when running the at least one program, is caused to perform a human posture detection method comprising:
• acquiring a plurality of frames of image data;
• acquiring a plurality of human posture reference maps output by a human posture detection model responsive to inputting a current frame of image data to the human posture detection model with reference to human posture confidence maps of a previous frame of image data, wherein different human posture reference maps correspond different human-posture key points;
• identifying a human-posture key point in each of the human posture reference maps;
• and
• generating human posture confidence maps of the current frame of image data based on credibility of the human-posture key points, wherein the human posture confidence maps of the current frame of image data is configured to participate in generation of human posture confidence maps of a next frame of image data,
• wherein the human posture detection model comprises a main path, a first branch, and a second branch, the main path comprising a residual module and an up-sampling module, the first branch comprising a refinement network module, and the second branch comprising a feedback module;
• and
• acquiring the plurality of human posture reference maps output by the human posture detection model responsive to inputting the current frame of image data into the human posture detection model with reference to the human posture confidence maps of the previous frame of image data comprises:
• acquiring a first convolution result output by the residual module based on a processing result of the residual module on the current frame of image data and a processing result of the feedback module on the human posture confidence maps of the previous image data;
• acquiring a second convolution result by inputting the first convolution result output by the residual module into the up-sampling module and processing the first convolution result therein, and acquiring a third convolution result by inputting the first convolution result output by the residual module into the refinement network module and processing the first convolution result therein;
• and
• acquiring the plurality of human posture reference maps by adding up the second convolution result and the third convolution result.

A non-transitory computer-readable storage medium storing at least one computer program therein, wherein the computer program, when run by a processor, causes the processor to perform a human posture detection method comprising:

• acquiring a plurality of frames of image data;
• acquiring a plurality of human posture reference maps output by a human posture detection model responsive to inputting a current frame of image data to the human posture detection model with reference to human posture confidence maps of a previous frame of image data, wherein different human posture reference maps correspond different human-posture key points;
• identifying a human-posture key point in each of the human posture reference maps;
• and
• generating human posture confidence maps of the current frame of image data based on credibility of the human-posture key points, wherein the human posture confidence maps of the current frame of image data is configured to participate in generation of human posture confidence maps of a next frame of image data,
• wherein the human posture detection model comprises a main path, a first branch, and a second branch, the main path comprising a residual module and an up-sampling module, the first branch comprising a refinement network module, and the second branch comprising a feedback module;
• and
• acquiring the plurality of human posture reference maps output by the human posture detection model responsive to inputting the current frame of image data into the human posture detection model with reference to the human posture confidence maps of the previous frame of image data comprises:
• acquiring a first convolution result output by the residual module based on a processing result of the residual module on the current frame of image data and a processing result of the feedback module on the human posture confidence maps of the previous image data;
• acquiring a second convolution result by inputting the first convolution result output by the residual module into the up-sampling module and processing the first convolution result therein, and acquiring a third convolution result by inputting the first convolution result output by the residual module into the refinement network module and processing the first convolution result therein;
• and
• acquiring the plurality of human posture reference maps by adding up the second convolution result and the third convolution result.