References of "Du, Wei"
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See detailSignSpeak -- Scientific Understanding and Vision-Based Technological Development for Continuous Sign Language Recognition and Translation
Dreuw, Philippe; Forster, Jens; Gweth, Yannick et al

in 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies (2010)

Detailed reference viewed: 40 (1 ULg)
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See detailVideo Analysis for Continuous Sign Language Recognition
Piater, Justus ULg; Hoyoux, Thomas ULg; Du, Wei ULg

in 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies (2010)

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See detailGround-Target Tracking in Multiple Cameras Using Collaborative Particle Filters and Principal Axis-Based Integration
Du, Wei ULg; Hayet, Jean-Bernard; Verly, Jacques ULg et al

in IPSJ Transactions on Computer Vision and Applications (2009), 1

Detailed reference viewed: 60 (2 ULg)
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See detailA Probabilistic Approach to Integrating Multiple Cues in Visual Tracking
Du, Wei ULg; Piater, Justus ULg

in 10th European Conference on Computer Vision (2008)

Detailed reference viewed: 19 (0 ULg)
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See detailSequential Variational Inference for Distributed Multi-Sensor Tracking and Fusion
Du, Wei ULg; Piater, Justus ULg

in The 10th International Conference on Information Fusion (2007)

Detailed reference viewed: 9 (1 ULg)
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See detailMulti-Camera People Tracking by Collaborative Particle Filters and Principal Axis-Based Integration
Du, Wei ULg; Piater, Justus ULg

in Asian Conference on Computer Vision (2007)

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See detailCollaborative Multi-Camera Tracking of Athletes in Team Sports
Du, Wei ULg; Hayet, Jean-Bernard; Piater, Justus ULg et al

in Workshop on Computer Vision Based Analysis in Sport Environments (CVBASE) (2006)

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See detailMulti-view object tracking using sequential belief propagation
Du, Wei ULg; Piater, Justus ULg

in Computer Vision – ACCV 2006 (2006)

Multiple cameras and collaboration between them make possible the integration of information available from multiple views and reduce the uncertainty due to occlusions. This paper presents a novel method ... [more ▼]

Multiple cameras and collaboration between them make possible the integration of information available from multiple views and reduce the uncertainty due to occlusions. This paper presents a novel method for integrating and tracking multi-view observations using bidirectional belief propagation. The method is based on a fully connected graphical model where target states at different views are represented as different but correlated random variables, and image observations at a given view are only associated with the target states at the same view. The tracking processes at different views collaborate with each other by exchanging information using a message passing scheme, which largely avoids propagating wrong information. An efficient sequential belief propagation algorithm is adopted to perform the collaboration and to infer the multi-view target states. We demonstrate the effectiveness of our method on video-surveillance sequences. [less ▲]

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See detailData Fusion by Belief Propagation for Multi-Camera Tracking
Du, Wei ULg; Piater, Justus ULg

in The 9th International Conference on Information Fusion (2006)

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See detailTracking by cluster analysis of feature points and multiple particle filters
Du, Wei ULg; Piater, Justus ULg

in Pattern Recognition and Image Analysis (2005)

A moving target produces a coherent cluster of feature points in the image plane. This motivates our novel method of tracking multiple targets by cluster analysis of feature points and Multiple particle ... [more ▼]

A moving target produces a coherent cluster of feature points in the image plane. This motivates our novel method of tracking multiple targets by cluster analysis of feature points and Multiple particle filters. First, feature points are detected by a Harris corner detector and tracked by a Lucas-Kanade tracker. Clusters of moving targets are then initialized by grouping spatially co-located points with similar motion using the EM algorithm. Due to the non-Gaussian distribution of the points in a cluster and the multi-modality resulting from multiple targets, multiple particle filters are applied to track all the clusters simultaneously: one particle filter is started for one cluster. The proposed method is well Suited for the typical video surveillance configuration where the cameras are still and targets of interest appear relatively small in the image. We demonstrate the effectiveness of our method on different PETS datasets. [less ▲]

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See detailTracking by cluster analysis of feature points and multiple particle filters
Du, Wei ULg; Piater, Justus ULg

in 3rd International Conference on Advances in Pattern Recognition (2005)

Detailed reference viewed: 9 (0 ULg)