On Uncertainties, Random Features and Object Tracking

Algorithms for probabilistic visual tracking hypothesize a distribution of the target state (location, scale, etc.) at every tracking step with an associated information content or equivalently, an uncertainty. One measure of this uncertainty is the differential entropy. In this paper, we present a...

Full description

Saved in:
Bibliographic Details
Main Authors: Badrinarayanan, V., Perez, P., Le Clerc, F., Oisel, L.
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Current measurement
Differential Entropy
Entropy
Extraterrestrial measurements
Filtering
Gaussian distribution
Measurement uncertainty
Monte Carlo methods
Probabilistic Filtering
Randomized features
Target tracking
Visual Tracking
Weight measurement
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Algorithms for probabilistic visual tracking hypothesize a distribution of the target state (location, scale, etc.) at every tracking step with an associated information content or equivalently, an uncertainty. One measure of this uncertainty is the differential entropy. In this paper, we present a unified way to approximate the differential entropy of tracking distributions, which then makes it suitable, among other factors, for a qualitative assessment of both deterministic and sequential Monte Carlo simulation based tracking algorithms. We then illustrate the usefulness of this assessment measure via tracking an object by choosing a set of randomly picked features on it, each individually tracked, removed according to an uncertainty analysis and replaced randomly, without any aid of a feature selection algorithm as in current use.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2007.4379765