Robust Unattended and Stolen Object Detection by Fusing Simple Algorithms

SanMiguel, Juan C.; Martinez, José M.

doi:10.1109/avss.2008.16

Cited by 38 publications

(25 citation statements)

References 21 publications

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“…Table 6: Recognition results for the analysis of controlled environments (Key: ABA: Abandoned-object, STO:Stolenobject, CSM2: rule-based approach [32]). Event detection examples for uncontrolled environments.…”

Section: Resultsmentioning

confidence: 99%

“…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%

“…For comparison purposes, we have selected [32] (hereafter called CSM2) that de nes a rule-based detection of the two complex events. It uses the following rules: the detection of a static blob (i.e., same position for 30 frames), the detection of the blob as non-people (by means of a speci c classi er), the detection of a person as the individual of the action and the use of edge and color information of the static blob to decide whether it is abandoned or stolen.…”

Section: Uncontrolled Environmentsmentioning

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: Resultsmentioning

confidence: 99%

Section: Uncontrolled Environmentsmentioning

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is composed of a sequential combination of the following stages: foreground detection, noise removal, blob extraction, blob tracking, static object detection, people recognition and event detection. Further details of the techniques implemented for each stage are provided in [32].…”

Section: Setupmentioning

confidence: 99%

“…For comparison purposes, we have selected a stateof-art approach for detecting abandoned objects in video surveillance [32] (from now on xed workow). It represents the related literature that is manually designed based on the expert knowledge of the task.…”

Section: Setupmentioning

confidence: 99%