期刊
2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
卷 -, 期 -, 页码 7033-7042出版社
IEEE
DOI: 10.1109/CVPR42600.2020.00706
关键词
-
资金
- National Science Foundation [1749376]
- Division Of Behavioral and Cognitive Sci
- Direct For Social, Behav & Economic Scie [1749376] Funding Source: National Science Foundation
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. UniPose incorporates contextual segmentation 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 filtering in the cascade architecture, while maintaining multi-scale fields-of-view comparable to spatial pyramid configurations. 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 results 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 single images and videos.
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