Results 1 - 10
of
439
Multimodal human computer interaction: A survey
, 2005
"... In this paper we review the major approaches to Multimodal Human Computer Interaction, giving an overview of the field from a computer vision perspective. In particular, we focus on body, gesture, gaze, and affective interaction (facial expression recognition and emotion in audio). We discuss user ..."
Abstract
-
Cited by 119 (3 self)
- Add to MetaCart
(Show Context)
In this paper we review the major approaches to Multimodal Human Computer Interaction, giving an overview of the field from a computer vision perspective. In particular, we focus on body, gesture, gaze, and affective interaction (facial expression recognition and emotion in audio). We discuss user and task modeling, and multimodal fusion, highlighting challenges, open issues, and emerging applications for Multimodal Human Computer Interaction (MMHCI) research.
A system for learning statistical motion patterns
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2006
"... permission from the publisher. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of th ..."
Abstract
-
Cited by 119 (1 self)
- Add to MetaCart
(Show Context)
permission from the publisher. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. © 2006 IEEE. Copyright and all rights therein are retained by authors or by other copyright holders. All persons downloading this information are expected to adhere to the terms and constraints invoked by copyright. This document or any part thereof may not be reposted without the explicit permission of the copyright holder. Citation for this copy:
A Survey of Vision-Based Methods for Action Representation, Segmentation and Recognition
, 2011
"... ..."
R.B.: Modelling crowd scenes for event detection
- In: Proceedings of the 18th International Conference on Pattern Recognition - Volume 01. ICPR ’06
, 2006
"... This paper is a postprint of a paper submitted to and accepted for publication in ICPR 2006 and is subject to IEEE copyright. This work presents an automatic technique for detection of abnormal events in crowds. Crowd behaviour is difficult to predict and might not be easily semantically translated. ..."
Abstract
-
Cited by 62 (0 self)
- Add to MetaCart
(Show Context)
This paper is a postprint of a paper submitted to and accepted for publication in ICPR 2006 and is subject to IEEE copyright. This work presents an automatic technique for detection of abnormal events in crowds. Crowd behaviour is difficult to predict and might not be easily semantically translated. Moreover it is difficulty to track individuals in the crowd using state of the art tracking algorithms. Therefore we characterise crowd behaviour by observing the crowd opti-cal flow and use unsupervised feature extraction to encode normal crowd behaviour. The unsupervised feature extrac-tion applies spectral clustering to find the optimal number of models to represent normal motion patterns. The mo-tion models are HMMs to cope with the variable number of motion samples that might be present in each observation window. The results on simulated crowds demonstrate the effectiveness of the approach for detecting crowd emergency scenarios. 1
The function space of an activity
- in Proc. Comput. Vis. Pattern Recognit
"... An activity consists of an actor performing a series of actions in a pre-defined temporal order. An action is an individual atomic unit of an activity. Different instances of the same activity may consist of varying relative speeds at which the various actions are executed, in addition to other intr ..."
Abstract
-
Cited by 61 (11 self)
- Add to MetaCart
(Show Context)
An activity consists of an actor performing a series of actions in a pre-defined temporal order. An action is an individual atomic unit of an activity. Different instances of the same activity may consist of varying relative speeds at which the various actions are executed, in addition to other intra- and inter- person variabilities. Most existing algorithms for activity recognition are not very robust to intra- and inter-personal changes of the same activity, and are extremely sensitive to warping of the temporal axis due to variations in speed profile. In this paper, we provide a systematic approach to learn the nature of such time warps while simultaneously allowing for the variations in descriptors for actions. For each activity we learn an ‘average ’ sequence that we denote as the nominal activity trajectory. We also learn a function space of time warpings for each activity separately. The model can be used to learn individualspecific warping patterns so that it may also be used for activity based person identification. The proposed model leads us to algorithms for learning a model for each activity, clustering activity sequences and activity recognition that are robust to temporal, intra- and inter-person variations. We provide experimental results using two datasets. 1.
2005a Affective multimodal human–computer interaction
- Proc
"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
Abstract
-
Cited by 60 (12 self)
- Add to MetaCart
(Show Context)
All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance
"... Abstract—This paper presents a survey of trajectory-based activity analysis for visual surveillance. It describes techniques that use trajectory data to define a general set of activities that are applicable to a wide range of scenes and environments. Events of interest are detected by building a ge ..."
Abstract
-
Cited by 57 (11 self)
- Add to MetaCart
(Show Context)
Abstract—This paper presents a survey of trajectory-based activity analysis for visual surveillance. It describes techniques that use trajectory data to define a general set of activities that are applicable to a wide range of scenes and environments. Events of interest are detected by building a generic topographical scene description from underlying motion structure as observed over time. The scene topology is automatically learned and is distinguished by points of interest and motion characterized by activity paths. The methods we review are intended for real-time surveillance through definition of a diverse set of events for further analysis triggering, including virtual fencing, speed profiling, behavior classification, anomaly detection, and object interaction. Index Terms—Event detection, motion analysis, situational awareness, statistical learning. Fig. 1. Relationship between analysis levels and required knowledge: highlevel activity analysis requires large amounts of domain knowledge while lowlevel analysis assumes very little. I.
Classifying spatiotemporal object trajectories using unsupervised learning of basis function coefficients
- In VSSN ’05: Proceedings of the third ACM international workshop on Video surveillance & sensor networks
, 2005
"... This paper proposes a novel technique for clustering and classification of object trajectory-based video motion clips using spatiotemporal functional approximations. A Mahalanobis classifier is then used for the detection of anomalous trajectories. Motion trajectories are considered as time series a ..."
Abstract
-
Cited by 54 (1 self)
- Add to MetaCart
(Show Context)
This paper proposes a novel technique for clustering and classification of object trajectory-based video motion clips using spatiotemporal functional approximations. A Mahalanobis classifier is then used for the detection of anomalous trajectories. Motion trajectories are considered as time series and modeled using the leading Fourier coefficients obtained by a Discrete Fourier Transform. Trajectory clustering is then carried out in the Fourier coefficient feature space to discover patterns of similar object motions. The coefficients of the basis functions are used as input feature vectors to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. Encoding trajectories in this way leads to efficiency gains over existing approaches that use discrete point-based flow vectors to represent the whole trajectory. Experiments are performed on two different datasets – synthetic and pedestrian object tracking- to demonstrate the effectiveness of our approach. Applications to motion data mining in video surveillance databases are envisaged.
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.
