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bmartacho / UniPose

We propose UniPose, a unified framework for human pose estimation, based on our “Waterfall” Atrous Spatial Pooling architecture, that achieves state-of-art-results on several pose estimation metrics. Current pose estimation methods utilizing standard CNN architectures heavily rely on statistical postprocessing or predefined anchor poses for joint localization. UniPose incorporates contextual seg- mentation and joint localization to estimate the human pose in a single stage, with high accuracy, without relying on statistical postprocessing methods. The Waterfall module in UniPose leverages the efficiency of progressive filter- ing in the cascade architecture, while maintaining multi- scale fields-of-view comparable to spatial pyramid config- urations. Additionally, our method is extended to UniPose- LSTM for multi-frame processing and achieves state-of-the- art results for temporal pose estimation in Video. Our re- sults on multiple datasets demonstrate that UniPose, with a ResNet backbone and Waterfall module, is a robust and efficient architecture for pose estimation obtaining state-of- the-art results in single person pose detection for both sin- gle images and videos.
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Local Maxima for Joints #19

Closed YuQi9797 closed 3 years ago

YuQi9797 commented 3 years ago

Hello, Author. Where is the code of Local Maxima for joints? image

is it there? https://github.com/bmartacho/UniPose/blob/c9ae3789d9da6313e6aa45c884607cbda61fcfef/utils/utils.py#L92

And can you share me the meanning of this? the return maxvals is confidence of keypoints? what the preds is? https://github.com/bmartacho/UniPose/blob/c9ae3789d9da6313e6aa45c884607cbda61fcfef/utils/evaluate.py#L63

bmartacho commented 3 years ago

The numpy functions numpy.amax and numpy.argmax return the location ID of the maximum response for the respective joints and its value (confidence), respectively.

https://github.com/bmartacho/UniPose/blob/c9ae3789d9da6313e6aa45c884607cbda61fcfef/utils/evaluate.py#L38

YuQi9797 commented 3 years ago

Hello Sir, https://github.com/bmartacho/UniPose/blob/c9ae3789d9da6313e6aa45c884607cbda61fcfef/utils/evaluate.py#L52 What the 'preds' it is? If it is the final corresponding map for joint prediction, why are you directly using the heat map of the model output in the test, rather than the final map of the prediction(preds)? https://github.com/bmartacho/UniPose/blob/c9ae3789d9da6313e6aa45c884607cbda61fcfef/unipose.py#L222