Sciweavers

Share
CVPR
2007
IEEE

Multi-class object tracking algorithm that handles fragmentation and grouping

11 years 1 months ago
Multi-class object tracking algorithm that handles fragmentation and grouping
We propose a framework for detecting and tracking multiple interacting objects, while explicitly handling the dual problems of fragmentation (an object may be broken into several blobs) and grouping (multiple objects may appear as a single blob). We use foreground blobs obtained by background subtraction from a stationary camera as measurements. The main challenge is to associate blob measurements with objects, given the fragment-object-group ambiguity when the number of objects is variable and unknown, and object-class-specific models are not available. We first track foreground blobs till they merge or split. We then build an inference graph representing merge-split relations between the tracked blobs. Using this graph and a generic object model based on spatial connectedness and coherent motion, we label the tracked blobs as whole objects, fragments of objects or groups of interacting objects. The outputs of our algorithm are entire tracks of objects, which may include correspondin...
Biswajit Bose, Xiaogang Wang, Eric Grimson
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Biswajit Bose, Xiaogang Wang, Eric Grimson
Comments (0)
books