A hierarchical Bayesian network for event recognition of human actions and interactions

By Sangho Park, Jake K. Aggarwal. 2004

242 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Human-Computer Interaction

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Citations in the corpus

Beyond Gaussian Pyramid: Multi-skip Feature Stacking for action recognition (2014, background): “This reduction is not possible in a naive approach.”
Recognition of Composite Human Activities through Context-Free Grammar Based Representation (2006, background): “Extending Park and Aggarwal’s work [ 6 ], Bayesian networks are implemented to estimate pose for each body part in each frame.”
Advances in View-Invariant Human Motion Analysis: A Review (2010, background): “In addition, Park and Aggarwal [99], [100] presented a three-level framework for recognizing human actions and interactions in video.”
Object tracking: A survey (2006, background): “Another approach to learn the different views of an object is by training a set of classifiers, for example, the support vector machines [Avidan 2001] or Bayesian networks [Park and Aggarwal 2004].”
"Here's looking at you, kid". Detecting people looking at each other in videos (2011, background): “This is very challenging video material with far greater variety in actors, shot editing, viewpoint, locations, lighting and clutter than the typical surveillance videos used previously for classifying interactions [3, 21, 29] where there is a fixed camera and scene.”
Efficient Interaction Recognition through Positive Action Representation (2013, unknown): “Human activity can be categorized according to complexity as partial body action [3], simple action [4], interaction activity [5, 6], or group activity [7].”
A Review on Human Activity Recognition Using Vision-Based Method (2017, background): “Park and Aggarwal [103] presented a hierarchical Bayesian network methodology for recognizing ﬁ ve two-person interactions.”
Human Action Recognition With Video Data: Research and Evaluation Challenges (2014, methodology): “Probabilistic-based methods such as Bayesian networks (see [25] and [65]–[67]) and Hidden Markov models (HMM) (see [20], [25], [67], and [68]) can also be used to create appearance models to model the limbs, heads, and torso.”
Ieee Transactions on Pattern Analysis an Machine Intelligence Structured Learning of Human Interactions in Tv Shows (methodology): “Moving towards more realistic datasets, Ryoo and Aggarwal [15] represented an interaction by modeling relations between pairs of spatio-temporal features using spatial and temporal predicates, such as " before " , " after " , " near " or " far " .”
A survey on activity recognition and behavior understanding in video surveillance (2013, methodology): “Ryoo and Aggarwal [156] presented an algorithm to estimate structural correspondence between two continuous videos.”