Name
Abstract | ||
|---|---|---|
In this paper, a bi-directional long-short term memory (LSTM) based approach is proposed for the estimation of missing body parts in a human pose estimation context. Accurate human pose estimation is often a key component for accurate human action and activity recognition. The key idea of our algorithm is to learn the temporal consistencies of the human body poses between previous and subsequent frames. This helps in estimating missing body parts and improves the general smoothness of the pose detection results. The approach acts as a post-processing step after the application of any off-the-shelf body part detector and has been evaluated on the PoseTrack dataset for both validation and testing sequences. The results show consistent improvement in the detection across all body parts. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/FG.2019.8756597 | 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019) |
Keywords | Field | DocType |
off-the-shelf body part detector,bidirectional LSTM,bi-directional long-short term memory based approach,human pose estimation,PoseTrack dataset,testing sequences,missing body part estimation,activity recognition,human action recognition | Activity recognition,Pattern recognition,Computer science,Pose,Artificial intelligence,Smoothness,Detector,Human body | Conference |
ISSN | ISBN | Citations |
2326-5396 | 978-1-7281-0090-6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ibrahim Radwan | 1 | 49 | 4.31 |
| Akshay Asthana | 2 | 729 | 25.02 |
| Hafsa Ismail | 3 | 1 | 1.70 |
| Byron Keating | 4 | 0 | 0.34 |
| Roland Goecke | 5 | 1323 | 69.44 |