1adrianb/binary-human-pose-estimation
This code implements a demo of the Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources paper by Adrian Bulat and Georgios Tzimiropoulos.
This tool helps researchers and developers in computer vision extract human pose information from images efficiently, especially on devices with limited computational power. It takes an image as input and outputs the detected keypoints for the human pose. This is ideal for those working with embedded systems or edge computing applications where resource constraints are a major concern.
214 stars. No commits in the last 6 months.
Use this if you need to detect human poses from images using a computationally lightweight method, suitable for devices with restricted processing power or memory.
Not ideal if you require the absolute highest accuracy for human pose estimation and have access to powerful computational resources.
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214
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68
Language
Lua
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Last pushed
Apr 27, 2021
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