Results 1 - 10
of
57
A Markov Clustering Topic Model for Mining Behaviour in Video
"... This paper addresses the problem of fully automated mining of public space video data. A novel Markov Clustering Topic Model (MCTM) is introduced which builds on existing Dynamic Bayesian Network models (e.g. HMMs) and Bayesian topic models (e.g. Latent Dirichlet Allocation), and overcomes their dra ..."
Abstract
-
Cited by 53 (6 self)
- Add to MetaCart
(Show Context)
This paper addresses the problem of fully automated mining of public space video data. A novel Markov Clustering Topic Model (MCTM) is introduced which builds on existing Dynamic Bayesian Network models (e.g. HMMs) and Bayesian topic models (e.g. Latent Dirichlet Allocation), and overcomes their drawbacks on accuracy, robustness and computational efficiency. Specifically, our model profiles complex dynamic scenes by robustly clustering visual events into activities and these activities into global behaviours, and correlates behaviours over time. A collapsed Gibbs sampler is derived for offline learning with unlabeled training data, and significantly, a new approximation to online Bayesian inference is formulated to enable dynamic scene understanding and behaviour mining in new video data online in real-time. The strength of this model is demonstrated by unsupervised learning of dynamic scene models, mining behaviours and detecting salient events in three complex and crowded public scenes. 1.
Global behaviour inference using probabilistic latent semantic analysis
- in: British Machine Vision Conference
, 2008
"... We present a novel framework for inferring global behaviour patterns through modelling behaviour correlations in a wide-area scene and detecting any anomaly in behaviours occurring both locally and globally. Specifically, we propose a semantic scene segmentation model to decompose a wide-area scene ..."
Abstract
-
Cited by 29 (10 self)
- Add to MetaCart
(Show Context)
We present a novel framework for inferring global behaviour patterns through modelling behaviour correlations in a wide-area scene and detecting any anomaly in behaviours occurring both locally and globally. Specifically, we propose a semantic scene segmentation model to decompose a wide-area scene into regions where behaviours share similar characteristic and are represented as classes of video events bearing similar features. To model behavioural correlations globally, we investigate both a probabilistic Latent Semantic Analysis (pLSA) model and a two-stage hierarchical pLSA model for global behaviour inference and anomaly detection. The proposed framework is validated by experiments using complex crowded outdoor scenes. 1
Scene segmentation for behaviour correlation
- In ECCV
, 2008
"... Abstract. This paper presents a novel framework for detecting abnormal pedestrian and vehicle behaviour by modelling cross-correlation among different co-occurring objects both locally and globally in a given scene. We address this problem by first segmenting a scene into semantic regions according ..."
Abstract
-
Cited by 28 (9 self)
- Add to MetaCart
(Show Context)
Abstract. This paper presents a novel framework for detecting abnormal pedestrian and vehicle behaviour by modelling cross-correlation among different co-occurring objects both locally and globally in a given scene. We address this problem by first segmenting a scene into semantic regions according to how object events occur globally in the scene, and second modelling concurrent correlations among regional object events both locally (within the same region) and globally (across different regions). Instead of tracking objects, the model represents behaviour based on classification of atomic video events, designed to be more suitable for analysing crowded scenes. The proposed system works in an unsupervised manner throughout using automatic model order selection to estimate its parameters given video data of a scene for a brief training period. We demonstrate the effectiveness of this system with experiments on public road traffic data. 1
Semi-supervised learning for anomalous trajectory detection
- In Proc. BMVC
, 2008
"... A novel learning framework is proposed for anomalous behaviour detection in a video surveillance scenario, so that a classifier which distinguishes between normal and anomalous behaviour patterns can be incrementally trained with the assistance of a human operator. We consider the behaviour of pedes ..."
Abstract
-
Cited by 21 (2 self)
- Add to MetaCart
(Show Context)
A novel learning framework is proposed for anomalous behaviour detection in a video surveillance scenario, so that a classifier which distinguishes between normal and anomalous behaviour patterns can be incrementally trained with the assistance of a human operator. We consider the behaviour of pedestrians in terms of motion trajectories, and parametrise these trajectories using the control points of approximating cubic spline curves. This paper demonstrates an incremental semi-supervised one-class learning procedure in which unlabelled trajectories are combined with occasional examples of normal behaviour labelled by a human operator. This procedure is found to be effective on two different datasets, indicating that a human operator could potentially train the system to detect anomalous behaviour by providing only occasional interventions (a small percentage of the total number of observations). 1
Probabilistic latent sequential motifs: Discovering temporal activity patterns in video scenes
- In British Machine Vision Conference (BMVC
, 2010
"... This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns from documents given as word-time occurrences. In this model, documents are represented as a mixture of sequential activity motifs (or topics) and their starting occurrences. The novelties ..."
Abstract
-
Cited by 17 (8 self)
- Add to MetaCart
(Show Context)
This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns from documents given as word-time occurrences. In this model, documents are represented as a mixture of sequential activity motifs (or topics) and their starting occurrences. The novelties are threefold. First, unlike previous ap-proaches where topics only modeled the co-occurrence of words at a given time instant, our topics model the co-occurrence and temporal order in which the words occur within a temporal window. Second, our model accounts for the important case where activities occur concurrently in the document. And third, our method explicitly models with latent variables the starting time of the activities within the documents, enabling to implicitly align the occurrences of the same pattern during the joint inference of the temporal topics and their starting times. The model and its robustness to the presence of noise have been validated on synthetic data. Its effectiveness is also illustrated in video activity analysis from low-level motion features, where the discovered topics capture frequent patterns that implicitly represent typical trajectories of scene objects. 1
Incremental activity modelling in multiple disjoint cameras
- IEEE Transactions on Pattern Analysis and Machine Intelligence
"... Abstract—Activity modeling and unusual event detection in a network of cameras is challenging, particularly when the camera views are not overlapped. We show that it is possible to detect unusual events in multiple disjoint cameras as context-incoherent patterns through incremental learning of time ..."
Abstract
-
Cited by 13 (7 self)
- Add to MetaCart
(Show Context)
Abstract—Activity modeling and unusual event detection in a network of cameras is challenging, particularly when the camera views are not overlapped. We show that it is possible to detect unusual events in multiple disjoint cameras as context-incoherent patterns through incremental learning of time delayed dependencies between distributed local activities observed within and across camera views. Specifically, we model multicamera activities using a Time Delayed Probabilistic Graphical Model (TD-PGM) with different nodes representing activities in different decomposed regions from different views and the directed links between nodes encoding their time delayed dependencies. To deal with visual context changes, we formulate a novel incremental learning method for modeling time delayed dependencies that change over time. We validate the effectiveness of the proposed approach using a synthetic data set and videos captured from a camera network installed at a busy underground station. Index Terms—Unusual event detection, multicamera activity modeling, time delay estimation, incremental structure learning. Ç 1
Exploring techniques for vision based human activity recognition: Methods, systems, and evaluation
- Sensors 2013
"... sensors ..."
(Show Context)
Detecting Abnormal Human behaviour using Multiple Cameras
"... In this work a bottom-up approach for human behaviour understanding is presented, using a multi-camera system. The proposed methodology classifies behaviour as normal or abnormal, by treating short-term behaviour classification and trajectory classification as two different classification problems. ..."
Abstract
-
Cited by 10 (2 self)
- Add to MetaCart
In this work a bottom-up approach for human behaviour understanding is presented, using a multi-camera system. The proposed methodology classifies behaviour as normal or abnormal, by treating short-term behaviour classification and trajectory classification as two different classification problems. Based on that assumption, a set of calculated features provide input to two one-class classifiers: a Support Vector Machine and a continuous Hidden Markov Model treated as an one-class classifier. An approximation algorithm, referring to the Forward Backward procedure of the continuous Hidden Markov Model, is also proposed to overcome numerical stability problems in the calculation of probability of emission for very long observations.
1 Mixture of Gaussians-based Background Subtraction for Bayer-Pattern Image Sequences
"... Abstract — This paper proposes a background subtraction method for Bayer-pattern image sequences. The proposed method models the background in a Bayer-pattern domain using a mixture of Gaussians (MoG) and classifies the foreground in an interpolated RGB domain. This method can achieve almost the sam ..."
Abstract
-
Cited by 9 (1 self)
- Add to MetaCart
(Show Context)
Abstract — This paper proposes a background subtraction method for Bayer-pattern image sequences. The proposed method models the background in a Bayer-pattern domain using a mixture of Gaussians (MoG) and classifies the foreground in an interpolated RGB domain. This method can achieve almost the same accuracy as MoG using RGB color images while maintaining computational resources (time and memory) similar to MoG using grayscale images. Experimental results show that the proposed method is a good solution to obtain high accuracy and low resource requirements simultaneously. This improvement is important for a low-level task like background subtraction since its accuracy affects the performance of high-level tasks, and is preferable for implementation in real-time embedded systems such as smart cameras.
A prototype learning framework using emd: Application to complex scenes analysis
- IEEE Trans. Pattern Anal. Mach. Intell
"... Abstract—In the last decades, many efforts have been devoted to develop methods for automatic scene understanding in the context of video surveillance applications. This paper presents a novel non-object centric approach for complex scene analysis. Similarly to previous methods, we use low-level cue ..."
Abstract
-
Cited by 7 (3 self)
- Add to MetaCart
(Show Context)
Abstract—In the last decades, many efforts have been devoted to develop methods for automatic scene understanding in the context of video surveillance applications. This paper presents a novel non-object centric approach for complex scene analysis. Similarly to previous methods, we use low-level cues to individuate atomic activities and create clip histograms. Differently from recent works, the task of discovering high-level activity patterns is formulated as a convex prototype learning problem. This problem results into a simple linear program that can be solved efficiently with standard solvers. The main advantage of our approach is that, using as objective function the Earth Mover’s Distance (EMD), the similarity among elementary activities is taken into account in the learning phase. To improve scalability we also consider some variants of EMD adopting L1 as ground distance for one and two dimensional, linear and circular histograms. In these cases only the similarity between neighboring atomic activities, corresponding to adjacent histogram bins, is taken into account. Therefore we also propose an automatic strategy for sorting atomic activities. Experimental results on publicly available datasets show that our method compares favorably with state-of-the-art approaches, often outperforming them.
