Human Action Recognition by Semilatent Topic Models

By Yang Wang, Greg Mori. 2009

326 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Variational Inference: A Review for Statisticians (2016, background): “As other examples, variational inference is important to probabilistic models of videos (Chan and Vasconcelos 2009; Wang and Mori 2009), image denoising (Likas and Galatsanos 2004), tracking (Vermaak, Lawrence, and Pérez 2003; Yu and Wu 2005), place recognition and mapping for robotics (Cummins and…”
Video In Sentences Out (2012, methodology): “…1995–1996) and recognition of action in video (Blank et al., 2005; Laptev et al., 2008; Liu et al., 2009; Rodriguez et al., 2008; Schuldt et al., 2004; Siskind and Morris, 1996; Starner et al., 1998; Wang and Mori, 2009; Xu et al., 2002, 2005) have been of considerable interest for a long time.”
Graph-Sparse LDA: A Topic Model with Structured Sparsity (2014, background): “LDA has been applied to a diverse set of tasks, including finding scientific topics in articles (Griffiths and Steyvers 2004), classifying images (Fei-Fei and Perona 2005), and recognizing human actions (Wang and Mori 2009).”
Modeling Temporal Structure of Decomposable Motion Segments for Activity Classification (2010, background): “This representation can be combined with either discriminative [4,5] classifiers, semi-latent topic models [6] or unsupervised generative [7,8] models.”
Learning Sparse Representations for Human Action Recognition (2012, methodology): “Weizmann Action [9], Weizmann Robustness [9], Ballet [10], UCF Sports [11] and Facial Expression dataset [8].”
Attribute Learning for Understanding Unstructured Social Activity (2012, methodology): “Probabilistic topic models [8] have been used quie extensively in modelling images [1] and video [14,15,9,7].”
A Critical Review of Action Recognition Benchmarks (2013, unknown): “Performances on these databases has saturated over the years, with perfect accuracy reported on the Weizmann set [40] and near-perfect accuracy on KTH (e.”
Beyond Actions: Discriminative Models for Contextual Group Activities (2010, unknown): “In action recognition, Wang and Mori [42] track individual people and model co-occurances of the actions in a single track”
Learning latent temporal structure for complex event detection (2012, methodology): “The representation has also been successfully used with semi-latent topic models [28] and unsupervised generative models [17].”
Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things (2020, background): “Generally, Turaga et al. [7] divided HAR into three levels as: 1) the movement recognition; 2) action recognition; and 3) activity recognition, which were referred to as the low-level vision [13], middle-level vision [14], and high-level vision, respectively [15].”
Slow Feature Analysis for Human Action Recognition (2012, methodology): “. As one of the most active topics in computer vision, many works on human action recognitionhavebeenreported[12],[13],[14],[15],[16],[17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36]. 1.1 Motivation and Overview In this paper, we ”
Max-margin Latent Dirichlet Allocation for Image Classification and Annotation (2011, background): “Much work [1, 3, 4, 12, 13, 20, 21] in image classification and labeling uses topic models, which are a class of powerful tools originally proposed in text modeling and have gained much popularity in computer vision recently.”
A temporal Bayesian model for classifying, detecting and localizing activities in video sequences (2012, methodology): “For example, Topic Modeling or Latent Dirichlet Allocation (LDA) [2] has been used to cluster low level features from videos into activities [25, 27, 24, 10].”