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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The performance when frequent parts occlusion occurrences #3
Thank you for this great work. It is amazing that your framework does not require separate branches for bounding box and joint detections. As is said in your paper, the task of human pose estimation is challenging due to the frequent occurrence of parts occlusion. So I really want to know the performance of UniPose in such occasions. Is there same case or comparison with other methods in sequential video or single image?
Dear,
Thank you for the feedback and interest on UniPose. We do not have a specific numeric study of the occlusions. The paper only publish the overall accuracy. Although, the comparison was made by visual inspection during development and testing.
Thank you for this great work. It is amazing that your framework does not require separate branches for bounding box and joint detections. As is said in your paper, the task of human pose estimation is challenging due to the frequent occurrence of parts occlusion. So I really want to know the performance of UniPose in such occasions. Is there same case or comparison with other methods in sequential video or single image?
Thank you.