Discovering Activities and Their Temporal Significance
18 Sep 2012-pp 240-245
TL;DR: An unsupervised model is proposed, called Time pLSA model, that extends the probabilistic Latent Semantic Analysis (pLSA) model to jointly capture the activities and their behaviour over time.
Abstract: In this paper, we address the problem of discovering activities and their temporal significance in an area under surveillance. Discovering activities along with its expectation of occurrence at a particular time plays an important role in many surveillance applications. We propose an unsupervised model, called Time pLSA model, that extends the probabilistic Latent Semantic Analysis (pLSA) model to jointly capture the activities and their behaviour over time. We use adaptive background subtraction to detect spatio-temporal patches, which are used as feature representation for activity patterns. Each of these patches are associated with the time slot in which they occur. Multinomial distributions are used to model both activities as distribution over spatio-temporal patches and time significance as distribution over the time-line. We demonstrate the effectiveness of our approach on a real life surveillance feed of an outdoor scene.
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TL;DR: In this study, the authors discuss various issues and problems in video analytics, proposed solutions and present some of the important current applications of video analytics.
Abstract: Video, rich in visual real-time content, is however, difficult to interpret and analyse. Video collections necessarily have large data volume. Video analytics strives to automatically discover patterns and correlations present in the large volume of video data, which can help the end-user to take informed and intelligent decisions as well as predict the future based on the patterns discovered across space and time. In this study, the authors discuss various issues and problems in video analytics, proposed solutions and present some of the important current applications of video analytics.
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Proceedings Article•
03 Jan 2001TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
Abstract: We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams [6], and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI) [3]. In the context of text modeling, our model posits that each document is generated as a mixture of topics, where the continuous-valued mixture proportions are distributed as a latent Dirichlet random variable. Inference and learning are carried out efficiently via variational algorithms. We present empirical results on applications of this model to problems in text modeling, collaborative filtering, and text classification.
25,546 citations
23 Jun 1999
TL;DR: This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model, resulting in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes.
Abstract: A common method for real-time segmentation of moving regions in image sequences involves "background subtraction", or thresholding the error between an estimate of the image without moving objects and the current image. The numerous approaches to this problem differ in the type of background model used and the procedure used to update the model. This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model. The Gaussian, distributions of the adaptive mixture model are then evaluated to determine which are most likely to result from a background process. Each pixel is classified based on whether the Gaussian distribution which represents it most effectively is considered part of the background model. This results in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. This system has been run almost continuously for 16 months, 24 hours a day, through rain and snow.
7,660 citations
"Discovering Activities and Their Te..." refers methods in this paper
...We first apply adaptive background subtraction [1] to the video and divide it into D number of video volumes each containing a fixed number of, say d, frames....
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TL;DR: This work considers problems involving groups of data where each observation within a group is a draw from a mixture model and where it is desirable to share mixture components between groups, and considers a hierarchical model, specifically one in which the base measure for the childDirichlet processes is itself distributed according to a Dirichlet process.
Abstract: We consider problems involving groups of data where each observation within a group is a draw from a mixture model and where it is desirable to share mixture components between groups. We assume that the number of mixture components is unknown a priori and is to be inferred from the data. In this setting it is natural to consider sets of Dirichlet processes, one for each group, where the well-known clustering property of the Dirichlet process provides a nonparametric prior for the number of mixture components within each group. Given our desire to tie the mixture models in the various groups, we consider a hierarchical model, specifically one in which the base measure for the child Dirichlet processes is itself distributed according to a Dirichlet process. Such a base measure being discrete, the child Dirichlet processes necessarily share atoms. Thus, as desired, the mixture models in the different groups necessarily share mixture components. We discuss representations of hierarchical Dirichlet processes ...
3,755 citations
Posted Content•
TL;DR: Probabilistic Latent Semantic Analysis (PLSA) as mentioned in this paper is a statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text and in related areas.
Abstract: Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent Semantic Analysis which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed method is based on a mixture decomposition derived from a latent class model. This results in a more principled approach which has a solid foundation in statistics. In order to avoid overfitting, we propose a widely applicable generalization of maximum likelihood model fitting by tempered EM. Our approach yields substantial and consistent improvements over Latent Semantic Analysis in a number of experiments.
2,233 citations
TL;DR: A novel unsupervised learning method for human action categories that can recognize and localize multiple actions in long and complex video sequences containing multiple motions.
Abstract: We present a novel unsupervised learning method for human action categories. A video sequence is represented as a collection of spatial-temporal words by extracting space-time interest points. The algorithm automatically learns the probability distributions of the spatial-temporal words and the intermediate topics corresponding to human action categories. This is achieved by using latent topic models such as the probabilistic Latent Semantic Analysis (pLSA) model and Latent Dirichlet Allocation (LDA). Our approach can handle noisy feature points arisen from dynamic background and moving cameras due to the application of the probabilistic models. Given a novel video sequence, the algorithm can categorize and localize the human action(s) contained in the video. We test our algorithm on three challenging datasets: the KTH human motion dataset, the Weizmann human action dataset, and a recent dataset of figure skating actions. Our results reflect the promise of such a simple approach. In addition, our algorithm can recognize and localize multiple actions in long and complex video sequences containing multiple motions.
1,440 citations