The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.06 - June (2001 vol.23)
pp: 560-576
ABSTRACT
<p><b>Abstract</b>—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.</p>
INDEX TERMS
Visual tracking, data association, color regions, textured regions, snakes.
CITATION
Christopher Rasmussen, Gregory D. Hager, "Probabilistic Data Association Methods for Tracking Complex Visual Objects", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.23, no. 6, pp. 560-576, June 2001, doi:10.1109/34.927458
18 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool