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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How do you deal with invisible point of data during training? #17
Hello author, may I ask how you deal with the invisible points in the process of the experiment? The provided code also appears to perform a series of operations, such as heat map generation, on the invisible points.
"Invisible" or occluded keypoints are still tentatively estimated by the network with the location where the method believes there is a higher likelihood of it occurring.
For the testing on the datasets used in this paper, occluded joints are detected but not counted for accuracy, as per pre-defined metrics by the dataset challenges.
In case you would like to not have the occluded joints in your visualization, one possible approach would be to set a minimum confidence threshold for the detected joints. I.e. only show joints with detection confidence >= 0.5.
Hello author, may I ask how you deal with the invisible points in the process of the experiment? The provided code also appears to perform a series of operations, such as heat map generation, on the invisible points.