Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143208
Title: Recognizing human actions as the evolution of pose estimation maps
Authors: Liu, Mengyuan
Yuan, Junsong
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Liu, M., & Yuan, J. (2018). Recognizing human actions as the evolution of pose estimation maps. Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1159-1168. doi:10.1109/cvpr.2018.00127
Abstract: Most video-based action recognition approaches choose to extract features from the whole video to recognize actions. The cluttered background and non-action motions limit the performances of these methods, since they lack the explicit modeling of human body movements. With recent advances of human pose estimation, this work presents a novel method to recognize human action as the evolution of pose estimation maps. Instead of relying on the inaccurate human poses estimated from videos, we observe that pose estimation maps, the byproduct of pose estimation, preserve richer cues of human body to benefit action recognition. Specifically, the evolution of pose estimation maps can be decomposed as an evolution of heatmaps, e.g., probabilistic maps, and an evolution of estimated 2D human poses, which denote the changes of body shape and body pose, respectively. Considering the sparse property of heatmap, we develop spatial rank pooling to aggregate the evolution of heatmaps as a body shape evolution image. As body shape evolution image does not differentiate body parts, we design body guided sampling to aggregate the evolution of poses as a body pose evolution image. The complementary properties between both types of images are explored by deep convolutional neural networks to predict action label. Experiments on NTU RGB+D, UTD-MHAD and PennAction datasets verify the effectiveness of our method, which outperforms most state-of-the-art methods.
URI: https://hdl.handle.net/10356/143208
ISBN: 978-1-5386-6421-6
DOI: 10.1109/cvpr.2018.00127
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/cvpr.2018.00127
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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