Skeleton-Based Online Action Prediction Using Scale Selection Network
By Jun Liu, Amir Shahroudy, G. Wang, Ling-yu Duan, Alex ChiChung Kot. 2019
151 citations recorded by the source corpus.
Action prediction is to recognize the class label of an ongoing activity when only a part of it is observed. In this paper, we focus on online action prediction in streaming 3D skeleton sequences. A dilated convolutional network is introduced to model the motion dynamics in temporal dimension via a sliding window over the temporal axis. Since there are significant temporal scale variations in the observed part of the ongoing action at different time steps, a novel window scale selection method is proposed to make our network focus on the performed part of the ongoing action and try to suppress the possible incoming interference from the previous actions at each step. An activation sharing scheme is also proposed to handle the overlapping computations among the adjacent time steps, which enables our framework to run more efficiently. Moreover, to enhance the performance of our framework for action prediction with the skeletal input data, a hierarchy of dilated tree convolutions are also designed to learn the multi-level structured semantic representations over the skeleton joints at each frame. Our proposed approach is evaluated on four challenging datasets. The extensive experiments demonstrate the effectiveness of our method for skeleton-based online action prediction.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, 3D Vision & Pose
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Citations in the corpus
- NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding (2019, methodology): “…Skeleton [26], the Spatio-Temporal LSTM [48], the Internal Feature Fusion [94], the GCA-LSTM [30], the Multi-Task Learning Network [49], the FSNet [103], [108], the Skeleton Visualization [104], the Two-Stream Attention LSTM [105], the Multi-Task CNN with RotClips [106], and the Body Pose…”
- Human Action Recognition From Various Data Modalities: A Review (2020, background): “The skeleton data has been shown to be a succinct yet informative representation for human behavior analysis [350], which, however, is a very sparse representation without the encoding of the shape information of the human body and the interacted”
- You2Me: Inferring Body Pose in Egocentric Video via First and Second Person Interactions (2019, background): “Recent approaches explore novel CNN-based methods, which have substantially improved the detection of visible body poses in images and video [10, 65, 71, 11, 34, 24, 18, 58, 36, 35, 30].”