Probabilistic data association methods for tracking complex visualobjects
Rasmussen, C.; Hager, G.D.
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Volume 23, Issue 6, Jun 2001 Page(s):560 - 576
Digital Object Identifier 10.1109/34.927458
Summary:We describe a framework that explicitly reasons about data
association to improve tracking performance in many difficult visual
environments. A hierarchy of tracking strategies results from ascribing
ambiguous or missing data to: 1) noise-like visual occurrences, 2)
persistent, known scene elements (i.e., other tracked objects), or 3)
persistent, unknown scene elements. First, we introduce a randomized
tracking algorithm adapted from an existing probabilistic data
association filter (PDAF) that is resistant to clutter and follows agile
motion. The algorithm is applied to three different tracking
modalities-homogeneous regions, textured regions, and snakes-and
extensibly defined for straightforward inclusion of other methods.
Second, we add the capacity to track multiple objects by adapting to
vision a joint PDAF which oversees correspondence choices between
same-modality trackers and image features. We then derive a related
technique that allows mixed tracker modalities and handles object
overlaps robustly. Finally, we represent complex objects as conjunctions
of cues that are diverse both geometrically (e.g., parts) and
qualitatively (e.g., attributes). Rigid and hinge constraints between
part trackers and multiple descriptive attributes for individual parts
render the whole object more distinctive, reducing susceptibility to
mistracking. Results are given for diverse objects such as people,
microscopic cells, and chess pieces
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