Uncertainty Control for Reliable Video Understanding on Complex Environments

Zúñiga, Marcos; Brémond, François; Thonnat, Monique

doi:10.5772/15606

Cited by 3 publications

(6 citation statements)

References 31 publications

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“…In a step further to bridge the gap between image-level data and high-level semantic information, this work extends previous work presented in [21] and [22] by integrating symbolic attribute information to the hierarchical model in a way that both numerical and symbolic attribute values can be in a common state model. The utilisation of symbolic attributes gives high flexibility to the approach, allowing the user to add significantly semantic attributes for assisting on scene interpretation.…”

Section: Introductionmentioning

confidence: 73%

Hierarchical and incremental event learning approach based on concept formation models

2013

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We propose an event learning approach for video, based on concept formation models. This approach incrementally learns on-line a hierarchy of states and event by aggregating the attribute values of tracked objects in the scene. The model can aggregate both numerical and symbolic values.The utilisation of symbolic attributes gives high flexibility to the approach. The approach also proposes the integration of attributes as a doublet value-reliability, for considering the effect in the event learning process of the uncertainty inherited from previous phases of the video analysis process.Simultaneously, the approach recognises the states and events of the tracked objects, giving a multi-level description the object situation.The approach has been evaluated for an elderly care application and a rat behaviour analysis application. The results show that the approach is capable of learning and recognising meaningful events occurring in the scene, and to build a rich model of the objects behaviour. The results also show that the approach can give a description of the activities of a person (e.g. approaching to a table, crouching), and to detect abnormal events based on the frequency of occurrence.

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Section: Introductionmentioning

confidence: 73%

Hierarchical and incremental event learning approach based on concept formation models

2013

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“…Just three expressions for w, l and x 3 were derived from the resolution of the system, as the other variables can be determined from the 10 equations previously discussed. For further details on the formulation of these equations, refer to [8].…”

Section: Classification Using 3d Generic Modelsmentioning

confidence: 99%

“…Each hypothesis set can be treated as a different tracking sub-problem, as one of the ways of controlling the combinatorial explosion of mobile hypotheses. Each hypothesis has associated a likelihood measure, as seen in equation (8).…”

Section: Hypothesis Representationmentioning

confidence: 99%

“…These measures represent the confidence on the visibility of each dimension of the parallelepiped and are described in Section 3.2.5. This parallelepiped model has been first introduced in [45], and more deeply discussed in [8]. The dimensions of the 3D model are calculated based on the 3D position of the vertexes of the parallelepiped in the world referential of the scene.…”

Section: Classification Using 3d Generic Modelsmentioning

confidence: 99%

“…Other beneficial characteristic of these measures is their capability to weight the contribution of noisy, erroneous or false data to better maintain the integrity of the object dynamics model. This article is focused on discussing in detail the proposed tracking approach, which has been previously introduced in [8] as a phase of an event learning approach. Therefore, the main contributions of the proposed tracking approach are: -a new algorithm for tracking multiple objects in noisy environments, -a new dynamics model driven by reliability measures for proper selection of valuable information extracted from noisy data and for representing erroneous and absent data, -the improved capability of MHT to manage multiple visual evidence-target associations, and -the combination of 2D image data with 3D information extracted using a generic classification model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real-time reliability measure-driven multi-hypothesis tracking using 2D and 3D features

Zúñiga

Brémond

Thonnat

2011

EURASIP J. Adv. Signal Process.

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We propose a new multi-target tracking approach, which is able to reliably track multiple objects even with poor segmentation results due to noisy environments. The approach takes advantage of a new dual object model combining 2D and 3D features through reliability measures. In order to obtain these 3D features, a new classifier associates an object class label to each moving region (e.g. person, vehicle), a parallelepiped model and visual reliability measures of its attributes. These reliability measures allow to properly weight the contribution of noisy, erroneous or false data in order to better maintain the integrity of the object dynamics model. Then, a new multitarget tracking algorithm uses these object descriptions to generate tracking hypotheses about the objects moving in the scene. This tracking approach is able to manage many-to-many visual target correspondences. For achieving this characteristic, the algorithm takes advantage of 3D models for merging dissociated visual evidence (moving regions) potentially corresponding to the same real object, according to previously obtained information. The tracking approach has been validated using video surveillance benchmarks publicly accessible. The obtained performance is real time and the results are competitive compared with other tracking algorithms, with minimal (or null) reconfiguration effort between different videos.

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Parallelepipedic shape modeling for moving objects in video surveillance systems

Hamida

Koubaa

Amar

et al. 2014

2014 Science and Information Conference

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Uncertainty Control for Reliable Video Understanding on Complex Environments

Cited by 3 publications

References 31 publications

Hierarchical and incremental event learning approach based on concept formation models

Hierarchical and incremental event learning approach based on concept formation models

Real-time reliability measure-driven multi-hypothesis tracking using 2D and 3D features

Parallelepipedic shape modeling for moving objects in video surveillance systems

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