Mining Motion Atoms and Phrases for Complex Action Recognition

By Limin Wang, Yu Qiao, Xiaoou Tang. 2013

90 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Unsupervised Learning of Long-Term Motion Dynamics for Videos (2017, background): “The past few years have seen great progress on activity recognition on short clips [13, 51, 28, 38, 40].”
Towards Good Practices for Very Deep Two-Stream ConvNets (2015, background): “The past several years have witnessed great progress on action recognition from short clips [8, 9, 12, 16, 17, 18, 19].”
Action recognition with trajectory-pooled deep-convolutional descriptors (2015, background): “To overcome this issue, several mid-level and high-level video representations have been proposed such as Action Bank [22], Dynamic-Poselets [37], Motionlets [35], Motion Atoms and Phrases [34], and Actons [42].”
Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice (2014, neutral): “[51] 2013 85.”
AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos (2016, methodology): “Other approaches have also used MIL and its variants to model discriminative frames in a video, with or without a temporal structure [26, 30, 8, 51, 42, 20, 27].”
A Robust and Efficient Video Representation for Action Recognition (2015, unknown): “Wang et al (2013b) propose motion atom and phrase as a mid-level temporal part for representing and classifying complex action, and achieve 84 .”
CUHK & SIAT Submission for THUMOS 15 Action Recognition Challenge (2015, background): “Human action recognition has been one of the most challenging problems in computer vision and received a great amount of research interests in recent years [6, 11, 12, 13, 15].”
Human Action Recognition in Unconstrained Videos by Explicit Motion Modeling (2015, background): “[40], and a mid-level representation called motion atom and phrase [65].”
Latent Hierarchical Model of Temporal Structure for Complex Activity Classification (2014, methodology): “In addition to these low-level local features and BoVW representation, there were some research works on mid-level and high-level representations such as motionlet [32], motion atom and phrase [15], action bank [33], and so on.”
What do 15,000 object categories tell us about classifying and localizing actions? (2015, unknown): “A video can be embedded in a representation of holistic actions [36, 38], or, attributes are defined on atomic parts of the action [53].”
Real-Time Action Recognition with Enhanced Motion Vector CNNs (2016, background): “[34, 30, 31] exploited mid-level representations by proposing MoFAP and Motionlets.”
Semantic Aware Video Transcription Using Random Forest Classifiers (2014, background): “Usual goal is activity or event classification of pre-defined categories [32,27,18].”
Video Action Detection with Relational Dynamic-Poselets (2014, methodology): “[26] designed a discriminative clustering method to discover the “temporal parts” of action, called motion atoms.”
Action Recognition with Stacked Fisher Vectors (2014, background): “More recently, many efforts have focused on developing mid-level representations [19,35,34,7,37,27] for action recognition.”
Simple to Complex Transfer Learning for Action Recognition (2016, methodology): “methods [4], [7]–[9], [27], [31], [32] have achieved promising performance in recognizing both simple and complex actions.”
Multi-view Super Vector for Action Recognition (2014, background): “Action recognition has been an active research area due to its wide applications [1, 33, 34, 36].”