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Selective Spatio-Temporal Aggregation Based Pose Refinement System: Towards Understanding Human Activities in Real-World Videos | | Di Yang
; Rui Dai
; Yaohui Wang
; Rupayan Mallick
; Luca Minciullo
; Gianpiero Francesca
; Francois Bremond
; | | Date: |
10 Nov 2020 | | Abstract: | Taking advantage of human pose data for understanding human activities has
attracted much attention these days. However, state-of-the-art pose estimators
struggle in obtaining high-quality 2D or 3D pose data due to occlusion,
truncation and low-resolution in real-world un-annotated videos. Hence, in this
work, we propose 1) a Selective Spatio-Temporal Aggregation mechanism, named
SST-A, that refines and smooths the keypoint locations extracted by multiple
expert pose estimators, 2) an effective weakly-supervised self-training
framework which leverages the aggregated poses as pseudo ground-truth instead
of handcrafted annotations for real-world pose estimation. Extensive
experiments are conducted for evaluating not only the upstream pose refinement
but also the downstream action recognition performance on four datasets, Toyota
Smarthome, NTU-RGB+D, Charades, and Kinetics-50. We demonstrate that the
skeleton data refined by our Pose-Refinement system (SSTA-PRS) is effective at
boosting various existing action recognition models, which achieves competitive
or state-of-the-art performance. | | Source: | arXiv, 2011.05358 | | Services: | Forum | Review | PDF | Favorites | |
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