Video understanding for complex activity recognition

Fusier, Florent; Valentin, Valéry; Brémond, François; Thonnat, Monique; Borg, Mark G.; Thirde, D.; Ferryman, James

doi:10.1007/s00138-006-0054-y

Cited by 61 publications

(30 citation statements)

References 17 publications

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“…For comparison purposes, we have selected the widely used CSM approach [3][23] [31][22] [32]. It de nes rules for each component of the event description that provide a binary decision on whether these components happened or not.…”

Section: Resultsmentioning

confidence: 99%

“…Among existing literature, Syntactic Models (SyM) represent complex events as hierarchical strings of symbols and detect them using simple routines such as Context-Free-Grammar (CFG) [21]. Moreover, Constraint Satisfaction Models (CSMs) de ne a set of rules derived from the hierarchical event description, among others, for airport monitoring [3], human-object interactions [22] and bank surveillance [23]. Another limitation of SM approaches is that they do not suggest the strategies to recognize the events from their descriptions.…”

Section: Semantic Modeling Approachesmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Semantic 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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“…For aircraft servicing, interpretation has the goal to recognise the various servicing activities at the apron position of an aircraft, beginning with arrival preparation, passenger disembarking via a passenger bridge, unloading and loading operations involving several kinds of vehicles, refuelling, catering and other activities. Our work can be seen as an alternative to an earlier approach reported in [1], which does not possess the innovative features reported here.…”

Section: Introductionmentioning

confidence: 93%

“…This paper is about realtime monitoring of object behaviour in aircraft servicing scenes, such as arrival preparation, unloading, tanking and others, based on video streams from several cameras 1 . The focus is on high-level interpretation of object tracks extracted from the video data.…”

Section: Introductionmentioning

confidence: 99%

Ontology-Based Realtime Activity Monitoring Using Beam Search

Bohlken

Neumann

Hotz

et al. 2011

Lecture Notes in Computer Science

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Abstract. In this contribution we present a realtime activity monitoring system, called SCENIOR (SCEne Interpretation with Ontology-based Rules) with several innovative features. Activity concepts are defined in an ontology using OWL, extended by SWRL rules for the temporal structure, and are automatically transformed into a high-level scene interpretation system based on JESS rules. Interpretation goals are transformed into hierarchical hypotheses structures associated with constraints and embedded in a probabilistic scene model. The incremental interpretation process is organised as a Beam Search with multiple parallel interpretation threads. At each step, a context-dependent probabilistic rating is computed for each partial interpretation reflecting the probability of that interpretation to reach completion. Low-rated threads are discarded depending on the beam width. Fully instantiated hypotheses may be used as input for higher-level hypotheses, thus realising a doubly hierarchical recognition process. Missing evidence may be "hallucinated" depending on the context. The system has been evaluated with real-life data of aircraft service activities.

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“…There is a lot of recent interest in the field of computer vision for using high-level context for interpreting scenes from a wide range of domains such as airport activity recognition (Fusier et al, 2007), interpreting building façades (Hotz et al, 2008;Čech anď Sára, 2007) or analysing traffic situations (Mohnhaupt and Neumann, 1993;Hummel et al, 2008). However, we are not aware of other current work combining different high-level and low-level sources of structure for interpreting highly structured domains.…”

Section: Introductionmentioning

confidence: 99%

INTERPRETING STRUCTURES IN MAN-MADE SCENES - Combining Low-Level and High-Level Structure Sources

Terzic¹,

Hotz²,

Šochman³

2010

Proceedings of the 2nd International Conference on Agents and Artificial Intelligence

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Abstract:Recognizing structure is an important aspect of interpreting many computer vision domains. Structure can manifest itself both visually, in terms of repeated low-level phenomena, and conceptually, in terms of a highlevel compositional hierarchy. In this paper, we demonstrate an approach for combining a low-level repetitive structure detector with a logical high-level interpretation system. We evaluate the performance on a set of images from the building façade domain.

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Video understanding for complex activity recognition

Cited by 61 publications

References 17 publications

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

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

Ontology-Based Realtime Activity Monitoring Using Beam Search

INTERPRETING STRUCTURES IN MAN-MADE SCENES - Combining Low-Level and High-Level Structure Sources

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