Computational Intelligence in Visual Sensor Networks: Improving Video Processing Systems

Patricio, Miguel Á.; Castanedo, Federico; Berlanga, Antonio; Pérez, Olga Gloria Barbón; García, Juan Carlos Figueroa

doi:10.1007/978-3-540-76827-2_14

Cited by 16 publications

(14 citation statements)

References 28 publications

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“…A typical configuration of processing modules in a surveillance-sensor agent is arranged in a pipe-line structure consisting of several modules [17] [18]. It directly interfaces with the image stream coming from a camera and extracts the tracking information of the mobile objects in the current frame.…”

Section: Acquiring Information Local Processingmentioning

confidence: 99%

A Multi-agent Architecture Based on the BDI Model for Data Fusion in Visual Sensor Networks

Castanedo

García

Patricio

et al. 2010

J Intell Robot Syst

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The newest surveillance applications attempt more complex tasks such as the analysis of the behavior of individuals and crowds. These complex tasks may use a distributed visual sensor network in order to gain coverage and exploit the inherent redundancy of the overlapped field of views. In this article, a Multi-agent architecture based on the Belief-Desire-Intention (BDI) model for processing the information and fuse data in a distributed visual sensor network is presented.Instead of exchanging raw images between the agents involved in the visual network, local signal processing is performed and only the key observed features are shared. After a registration or calibration phase, the proposed architecture performs tracking, data fusion and coordination. Using the proposed Multi-agent architecture, we focus on the means of fusing the estimated positions on the ground plane from different agents which are applied to the same object. This fusion process is used for two different purposes:(1) to obtain a continuity in the tracking along the field of view of the cameras involved in the distributed network, (2) to improve the quality of the tracking by means of fusion techniques, and by discarding non reliable sensors.Experimental results on two different scenarios show that the designed architecture can successfully track an object even when occlusions or sensor's errors take place. The sensor's errors are reduced by exploiting the inherent redundancy of a visual sensor network with overlapped field of views.

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Section: Acquiring Information Local Processingmentioning

confidence: 99%

A Multi-agent Architecture Based on the BDI Model for Data Fusion in Visual Sensor Networks

Castanedo

García

Patricio

et al. 2010

J Intell Robot Syst

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“…The performance of a video tracking system implementation depends on two strictly coupled subtasks: the data association method used for assigning observations to tracks and the model selected to estimate the movement of an object (Patricio et al 2008). Tracking algorithms can be formulated as the correspondence of detected objects represented by tracks across frames.…”

Section: Related Workmentioning

confidence: 99%

Boosting video tracking performance by means of Tabu Search in intelligent visual surveillance systems

et al. 2010

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In this paper, we present a fast and efficient technique for the data association problem applied to visual tracking systems. Visual tracking process is formulated as a combinatorial hypotheses search with a heuristic evaluation function taking into account structural and specific information such as distance, shape, color, etc.We introduce a Tabu Search algorithm which performs a search on an indirect space. A novel problem formulation allows us to transform any solution into the real search space, which is needed for fitness calculation, in linear time. This new formulation and the use of auxiliary structures yields a fast transformation from a blob-to-track assignment space to the real shape and position of tracks space (while calculating fitness in an incremental fashion), which is key in order to produce efficient and fast results. Other previous approaches are based on statistical techniques or on evolutionary algorithms. These techniques are quite efficient and robust although they cannot converge as fast as our approach.

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“…Tracking is a complex problem, since several difficulties can arise as a result of the imperfect functioning of video sensors and the wide range of possible scenes. Numerous soft computing techniques have been proposed to improve the results of tracking systems [2].…”

Section: Introductionmentioning

confidence: 99%

Towards Interoperability in Tracking Systems: An Ontology-Based Approach

Gómez‐Romero

Patricio

García

et al. 2009

Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira’s Scientific Legacy

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Abstract. Current video surveillance systems are expected to allow for management of video sequences recorded by distributed cameras. In order to obtain a complete view of the scene, it is necessary to apply data fusion techniques to the acquired data. This increases the need of communicating various and probably heterogeneous components in a vision system. A solution is to use a common and well-defined vocabulary to achieve understanding between them. In this work, we present an OWL ontology aimed at the symbolic representation of tracking data. The ontological representation of tracking data improves system interoperability and scalability, and facilitates the development of new functionalities. The ontology can be used to express the tracking information contained in the messages exchanged by the agents in CS-MAS, a multiagent framework for the development of multi-camera vision systems.

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Computational Intelligence in Visual Sensor Networks: Improving Video Processing Systems

Cited by 16 publications

References 28 publications

A Multi-agent Architecture Based on the BDI Model for Data Fusion in Visual Sensor Networks

A Multi-agent Architecture Based on the BDI Model for Data Fusion in Visual Sensor Networks

Boosting video tracking performance by means of Tabu Search in intelligent visual surveillance systems

Towards Interoperability in Tracking Systems: An Ontology-Based Approach

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