Video Analysis for Continuous Sign Language Recognition

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

A Probabilistic Approach to Integrating Multiple Cues in Visual Tracking

in 10th European Conference on Computer Vision (2008)

Multi-Camera People Tracking by Collaborative Particle Filters and Principal Axis-Based Integration

in Asian Conference on Computer Vision (2007)

Multi-view object tracking using sequential belief propagation

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 ▲]

Tracking by cluster analysis of feature points and multiple particle filters

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 ▲]