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R.S. Verver
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Efficient Embedded mmWave Human Pose Estimation
The Effects of Component Size on Model Accuracy, Latency and Memory Usage
Human-pose estimation is a technology with many applications such as healthcare, smart homes, and new methods of human-computer interaction. However, traditional RGB camera-based systems come with significant privacy risks and can perform poorly in dark rooms. A new approach to human-pose estimation, estimating through the use of mmWave radars, could solve these problems. mmWave creates a point cloud of a person, rather than a direct RGB image, and is therefore not affected by dark conditions, while simultaneously letting the subject stay anonymous. Current mmWave models are very accurate, on the order of centimetres, but generally too costly to run without a GPU.
In this paper, we create an optimised mmWave human-pose estimation model that runs more accurately without a GPU compared to a baseline model. We do this by analysing a baseline model to find which parts can be compressed without excessively losing accuracy.
Our improved model has an inference time of 41 ms with a Mean Absolute Error (MAE) of 7.72 cm on an embedded device. Compared to the baseline, this model saves 85.9% latency, at the cost of 4.8% MAE accuracy.
Through finding which parts can be compressed most effectively, we also gain insight into the relative importance of each component of the model. We also identify components that, with further research, could be improved to increase the accuracy of the model. ...
In this paper, we create an optimised mmWave human-pose estimation model that runs more accurately without a GPU compared to a baseline model. We do this by analysing a baseline model to find which parts can be compressed without excessively losing accuracy.
Our improved model has an inference time of 41 ms with a Mean Absolute Error (MAE) of 7.72 cm on an embedded device. Compared to the baseline, this model saves 85.9% latency, at the cost of 4.8% MAE accuracy.
Through finding which parts can be compressed most effectively, we also gain insight into the relative importance of each component of the model. We also identify components that, with further research, could be improved to increase the accuracy of the model. ...
Human-pose estimation is a technology with many applications such as healthcare, smart homes, and new methods of human-computer interaction. However, traditional RGB camera-based systems come with significant privacy risks and can perform poorly in dark rooms. A new approach to human-pose estimation, estimating through the use of mmWave radars, could solve these problems. mmWave creates a point cloud of a person, rather than a direct RGB image, and is therefore not affected by dark conditions, while simultaneously letting the subject stay anonymous. Current mmWave models are very accurate, on the order of centimetres, but generally too costly to run without a GPU.
In this paper, we create an optimised mmWave human-pose estimation model that runs more accurately without a GPU compared to a baseline model. We do this by analysing a baseline model to find which parts can be compressed without excessively losing accuracy.
Our improved model has an inference time of 41 ms with a Mean Absolute Error (MAE) of 7.72 cm on an embedded device. Compared to the baseline, this model saves 85.9% latency, at the cost of 4.8% MAE accuracy.
Through finding which parts can be compressed most effectively, we also gain insight into the relative importance of each component of the model. We also identify components that, with further research, could be improved to increase the accuracy of the model.
In this paper, we create an optimised mmWave human-pose estimation model that runs more accurately without a GPU compared to a baseline model. We do this by analysing a baseline model to find which parts can be compressed without excessively losing accuracy.
Our improved model has an inference time of 41 ms with a Mean Absolute Error (MAE) of 7.72 cm on an embedded device. Compared to the baseline, this model saves 85.9% latency, at the cost of 4.8% MAE accuracy.
Through finding which parts can be compressed most effectively, we also gain insight into the relative importance of each component of the model. We also identify components that, with further research, could be improved to increase the accuracy of the model.