A Framework Dealing with Uncertainty for Complex Event Recognition

Romdhane, Rim; Brémond, François; Thonnat, Monique

doi:10.1109/avss.2010.39

Cited by 9 publications

(8 citation statements)

References 11 publications

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“…Based on the algorithms proposed in [10] dealing with the event recognition process, we propose an extension for human posture detection using a multi-sensor approach.…”

Section: Instantaneous Likelihood Function For Primitive Statesmentioning

confidence: 99%

“…For all feature values including the example feature in our case (sitting-standing posture's height), the instantaneous likelihood of the test video is computed for each frame using Equation 3 [10] with the Gaussian parameters previously obtained.…”

Section: Instantaneous Likelihood Function For Primitive Statesmentioning

confidence: 99%

“…Methods based on mono-sensor rely on image analysis have been carried out using different approaches like probabilistic approach [ 5 -7 ] or constraint-based approach [8] [9] . The constraint-based approach proposed by Romdhane et al [10] handles the uncertainty of the activity recognition in complex event using probabilistic reasoning. It provides a convenient mechanism of reasoning to handle event uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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Fusion Framework for Video Event Recognition

Ma¹,

Fosty²,

Crispim-Junior³

et al. 2013

Computer Graphics and Imaging / 798: Signal Processing, Pattern Recognition and Applications

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This paper presents a multisensor fusion framework for video activities recognition based on statistical reasoning and D-S evidence theory. Precisely, the framework consists in the combination of the events' uncertainty computation with the trained database and the fusion method based on the conflict management of evidences. Our framework aims to build Multisensor fusion architecture for event recognition by combining sensors, dealing with conflicting recognition, and improving their performance. According to a complex event's hierarchy, Primitive state is chosen as our target event in the framework. A RGB camera and a RGB-D camera are used to recognise a person's basic activities in the scene. The main convenience of the proposed framework is that it firstly allows adding easily more possible events into the system with a complete structure for handling uncertainty. And secondly, the inference of DempsterShafer theory resembles human perception and fits for uncertainty and conflict management with incomplete information. The cross-validation of real-world data (10 persons) is carried out using the proposed framework, and the evaluation shows promising results that the fusion approach has an average sensitivity of 93.31% and an average precision of 86.7%. These results are better than the ones when only one camera is used, encouraging further research focusing on the combination of more sensors with more events, as well as the optimization of the parameters in the framework for improvements.

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“…Based on the algorithms proposed in [10] dealing with the event recognition process, we propose an extension for human posture detection using a multi-sensor approach.…”

Section: Instantaneous Likelihood Function For Primitive Statesmentioning

confidence: 99%

Section: Instantaneous Likelihood Function For Primitive Statesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fusion Framework for Video Event Recognition

Ma¹,

Fosty²,

Crispim-Junior³

et al. 2013

Computer Graphics and Imaging / 798: Signal Processing, Pattern Recognition and Applications

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“…Automatic activity recognition is a very important and active area of research [7], [5], [19]. Activity recognition approaches can be divided into two main approaches: probabilistic approaches and description-based approaches.…”

Section: Related Workmentioning

confidence: 99%

Activity recognition and uncertain knowledge in video scenes

Romdhane

Crispim

Brémond

et al. 2013

2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance

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Activity recognition has been a growing research topic in the last years and its application varies from automatic recognition of social interaction such as shaking hands, parking lot surveillance, traffic monitoring and the detection of abandoned luggage. This paper describes a probabilistic framework for uncertainty handling in a description-based event recognition approach. The proposed approach allows the flexible modeling of composite events with complex temporal constraints. It uses probability theory to provide a consistent framework for dealing with uncertain knowledge for the recognition of complex events. We validate the event recognition accuracy of the proposed algorithm on real-world videos. The experimental results show that our system can successfully recognize activities with a high recognition rate. We conclude by comparing our algorithm with the state of the art and showing how the definition of event models and the probabilistic reasoning can influence the results of real-time event recognition.

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“…Probabilistic extensions are proposed to handle the uncertainty of simple event de nitions for PNs [25] and CSMs [26]. In both approaches, the objective is to de ne a certainty measure for the observations associated to the event description components (e.g., map the certainty of a person being close to a zone of interest by using sigmoid [25] or Gaussian [26] functions). However, they do not consider the low-level uncertainty and they do not de ne the relation between the event descriptions and their recognition strategies.…”

Section: Hybrid Modeling Approachesmentioning

confidence: 99%

A semantic-based probabilistic approach for real-time video event recognition

SanMiguel¹,

Martínez²

2012

Computer Vision and Image Understanding

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El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription This paper presents an approach for real-time video event recognition that combines the accuracy and descriptive capabilities of, respectively, probabilistic and semantic approaches. Based on a state-of-art knowledge representation, we de ne a methodology for building recognition strategies from event descriptions that consider the uncertainty of the low-level analysis. Then, we e ciently organize such strategies for performing the recognition according to the temporal characteristics of events. In particular, we use Bayesian Networks and probabilistically-extended Petri Nets for recognizing, respectively, simple and complex events. For demonstrating the proposed approach, a framework has been implemented for recognizing human-object interactions in the video monitoring domain. The experimental results show that our approach improves the event recognition performance as compared to the widely used deterministic approach.Keywords: Video event detection, Semantic video analysis, Bayes Network, Petri Net, Low-level uncertaintyThe recognition of human-related events has recently become a relevant research area motivated by the variety of promising applications such as video surveillance, human-computer interaction and content-based indexing. Moreover, this interest can be also explained by the maturity of the employed low-level tools. Nevertheless, it still presents many challenges such as the uncertainty of the low-level tools (e.g., object detection and tracking), the limited availability of training data, the similar appearance of di erent events and the modeling of complex relations.Many approaches have been proposed for event recognition which can be roughly classi ed into semantic and probabilistic. Semantic (or deterministic) approaches are based on de ning rules to model the events [1]. However, current approaches only describe a small portion of semantics (e.g., $ This work has been partially supported by the Spanish Administration agency CDTI (CENIT-VISION 2007-1007 On the other hand, the probabilistic approaches have shown a superior performance as compared to the semantic ones [4]. They accurately learn event models from training data achieving high precision within a domain and allowing an intrinsic uncertainty handling. However, they are not able to model complex relations and their usage is limited for di erent, albeit related, domains. In this situation, a combination of both approaches would be desirable for solving these limitations. Although this combination is gaining attention in the recent years, current approaches are limited to the de nition of simple events [5], the assumption of accurate low-level analysis [6] and the use of domain-dependent recognition strategies [7]. Thus, their extension to generic recognition of complex events considering low-level uncertainty is not a straightforward task. This paper addresses the above-mentioned limitations by introducin...

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A Framework Dealing with Uncertainty for Complex Event Recognition

Cited by 9 publications

References 11 publications

Fusion Framework for Video Event Recognition

Fusion Framework for Video Event Recognition

Activity recognition and uncertain knowledge in video scenes

A semantic-based probabilistic approach for real-time video event recognition

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