Concurrent Action Detection with Structural Prediction

By Ping Wei, Nanning Zheng, Yibiao Zhao, Song-Chun Zhu. 2013

70 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

RGB-D-based action recognition datasets: A survey (2016, background): “Concurrent action The concurrent action dataset [24] (http://www.”
Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “[171] Structural Prediction Conc Lowlv Hand X X”
Skeleton-Based Online Action Prediction Using Scale Selection Network (2019, background): “Beside the online action prediction task, the problem of temporal action detection [22], [56], [56], [57], [58], [59], [60], [61], [62], [63],”
PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding (2017, methodology): “Most methods are designed for offline action detection [38, 31, 50].”
Online Human Action Detection using Joint Classification-Regression Recurrent Neural Networks (2016, methodology): “Most methods are designed for action recognition [13,14,17], i.e., to recognize the action from a well-segmented sequence, or ofﬂine action detection [18,10,8,19].”
A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets (2016, methodology): “We evaluate this task on the Concurrent Actions dataset [35], which provides 61 RGBD videos and pose estimation data annotated with 12 action categories.”
Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data (2016, background): “Joint-based approaches consider human skeleton as a set of points, and represent it using features like joint positions [5, 8, 9, 13, 16, 21, 26], joint orientations in a fixed coordinate system [24, 37, 32, 22], pairwise relative joint positions [34, 35, 36, 39], etc.”
Simultaneous Feature and Body-Part Learning for real-time robot awareness of human behaviors (2017, background): “Wei et al. [14] developed wavelet features to represent a sequence of 3D skeletal joints, and a concurrent action detection model to understand human behaviors.”
3D skeleton-based human action classification: A survey (2016, methodology): “In [97], the sequences are resampled in order to obtain representations of comparable length.”
SSNet: Scale Selection Network for Online 3D Action Prediction (2018, background): “Beside the online action prediction task, the problem of temporal action detection [68, 73, 48, 33, 7, 53, 68, 14, 54, 16, 15, 65, 82] also copes with untrimmed videos.”
ActivityNet: A large-scale video benchmark for human activity understanding (2015, background): “Another dimension of complexity is addressed by datasets that focus on composable [21] and concurrent [41] activities, but these are constrained with respect to the scene and environment assumptions.”
SceneGrok (2014, background): “…new; much prior work has targeted this domain [Gibson 1977; Gupta et al. 2011; Pandey and Alami 2012; Fouhey et al. 2012; Koppula et al. 2013; Koppula and Saxena 2013; ACM Transactions on Graphics, Vol. 33, No. 6, Article 212, Publication Date: November 2014 Jiang et al. 2013; Wei et al. 2013a].”