Recognition of group activities using dynamic probabilistic networks

By Shaogang Gong, Tao A. Xiang. 2003

307 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders (2018, neutral): “Related work pre-dating deep learning includes random graphs (Erdos & R´enyi, 1960; Barab´asi & Albert, 1999), stochastic blockmodels (Snijders & Nowicki, 1997), or state transition matrix learning (Gong & Xiang, 2003).”
Anticipating Human Activities Using Object Affordances for Reactive Robotic Response (2013, methodology): “Such frameworks as HMMs [53], [54], DBNs [55], CRFs [5], [56], [57], semi-CRFs [58] have been previously used to model the temporal structure of videos and text.”
Understanding Transit Scenes: A Survey on Human Behavior-Recognition Algorithms (2010, background): “detection [166], [167], as well as events based on group activities [165].”
A survey on activity recognition and behavior understanding in video surveillance (2013, methodology): “Gong and Xiang [52] proposed a dynamically multi-linked Hidden Markov model with the structure determined by Bayesian Information Criterion (BIC) to automatically summaries or categories activities within video.”
Modelling Crowd Scenes for Event Detection (2006, background): “The application domains for these techniques usually involve simple environments such as offices [8], kitchens [3] , cargo bays [6] and loading docks [5] such that activity recognition is focused upon modeling the actions and interactions of small groups of people/objects.”
Human activity analysis (2011, methodology): “First of all, researchers have focused on the recognition of group activities where each group member has its own role different from the others [Cupillard et al. 2002; Gong and Xiang 2003; Lv et al. 2004; Zhang et al. 2006; Dai et al. 2008].”
Machine Recognition of Human Activities: A Survey (2008, background): “issue Gong et al [108] presented a DBN framework where the structure of the network is discovered automatically us ing Bayesian Information Criterion [109], [110].”
Understanding Video Events: A Survey of Methods for Automatic Interpretation of Semantic Occurrences in Video (2009, unknown): “Dynamic multilevel HMMs (DML-HMMs) [79]”
Conditional models for contextual human motion recognition (2006, background): “…have a natural compositional (or concurrent) structure in terms of basic action units (run and hand-wave, walk and shake hands while involved in a conversation with somebody known) and because even the transition between simple activities naturally has temporal segments of ambiguity and overlap.”
Video Behavior Profiling for Anomaly Detection (2008, methodology): “By doing so, robust behavior recognition and anomaly detection are ensured at the shortest possible time, as opposed to previous work such as [2], [8], [16], which requires completed behavior patterns to be observed.”
Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos (2009, background): “There has been significant research in using contextual models for action recognition [10] [20] [9] [2] [16].”
Recognizing linked events: Searching the space of feasible explanations (2009, unknown): “We provide experimental results for a challenging two-layer linkage problem, demonstrating the ability to recognise and link drop and pick events of bicycles in a rack over five days.”