A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs

This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns called motifs from documents given as word time count matrices (e.g., videos). In this model, documents are represented as a mixture of sequential activity patterns (our motifs) where th...

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Vydané v:	International journal of computer vision Ročník 103; číslo 1; s. 100 - 126
Hlavní autori:	Varadarajan, Jagannadan, Emonet, Rémi, Odobez, Jean-Marc
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Boston Springer US 01.05.2013 Springer Springer Nature B.V Springer Verlag
Predmet:	Algorithms Analysis Applied sciences Artificial Intelligence Cameras Computer Imaging Computer Science Computer science; control theory; systems Computer Vision and Pattern Recognition Consumer goods Data processing. List processing. Character string processing Exact sciences and technology Image Processing and Computer Vision Inference Innovations Machine Learning Mathematical analysis Mathematical models Memory organisation. Data processing Mineral industry Mining industry Networks Pattern Recognition Pattern Recognition and Graphics Pattern recognition. Digital image processing. Computational geometry Semantics Software Sparsity Speech and sound recognition and synthesis. Linguistics Studies Surveillance Temporal logic Vision Vision systems Windows (intervals) Activity analysis Video surveillance Unsupervised Latent sequential patterns Topic models PLSA Recurrence Mixing Video signal Activity Data mining Modeling Recovery Optimization Hidden variables Data distribution Linguistics Bayes network Natural language Overlay Computer vision Probabilistic approach Inference Synchronization Long term Surveillance Scene analysis Document structure Cooccurrence analysis
ISSN:	0920-5691, 1573-1405
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Popis
Shrnutí:	This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns called motifs from documents given as word time count matrices (e.g., videos). In this model, documents are represented as a mixture of sequential activity patterns (our motifs) where the mixing weights are defined by the motif starting time occurrences. The novelties are multi fold. First, unlike previous approaches where topics modeled only the co-occurrence of words at a given time instant, our motifs model the co-occurrence and temporal order in which the words occur within a temporal window. Second, unlike traditional Dynamic Bayesian networks (DBN), our model accounts for the important case where activities occur concurrently in the video (but not necessarily in synchrony), i.e., the advent of activity motifs can overlap. The learning of the motifs in these difficult situations is made possible thanks to the introduction of latent variables representing the activity starting times, enabling us to implicitly align the occurrences of the same pattern during the joint inference of the motifs and their starting times. As a third novelty, we propose a general method that favors the recovery of sparse distributions, a highly desirable property in many topic model applications, by adding simple regularization constraints on the searched distributions to the data likelihood optimization criteria. We substantiate our claims with experiments on synthetic data to demonstrate the algorithm behavior, and on four video datasets with significant variations in their activity content obtained from static cameras. We observe that using low-level motion features from videos, our algorithm is able to capture sequential patterns that implicitly represent typical trajectories of scene objects.
Bibliografia:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23
ISSN:	0920-5691 1573-1405
DOI:	10.1007/s11263-012-0596-6