Journal ArticleDOI
A Probabilistic Exclusion Principle for Tracking Multiple Objects
John MacCormick,Andrew Blake +1 more
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TLDR
An observation density for tracking is presented which solves this problem by exhibiting a probabilistic exclusion principle, and is presented of partitioned sampling, a new sampling method for multiple object tracking.Abstract:
Tracking multiple targets is a challenging problem, especially when the targets are “identical”, in the sense that the same model is used to describe each target. In this case, simply instantiating several independent 1-body trackers is not an adequate solution, because the independent trackers tend to coalesce onto the best-fitting target. This paper presents an observation density for tracking which solves this problem by exhibiting a probabilistic exclusion principle. Exclusion arises naturally from a systematic derivation of the observation density, without relying on heuristics. Another important contribution of the paper is the presentation of partitioned sampling, a new sampling method for multiple object tracking. Partitioned sampling avoids the high computational load associated with fully coupled trackers, while retaining the desirable properties of coupling.read more
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Object tracking: A survey
TL;DR: The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends to discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.
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Kernel-based object tracking
TL;DR: A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed, which employs a metric derived from the Bhattacharyya coefficient as similarity measure, and uses the mean shift procedure to perform the optimization.
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Monocular Pedestrian Detection: Survey and Experiments
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An adaptive color-based particle filter
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References
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Probability and Measure
TL;DR: In this paper, the convergence of distributions is considered in the context of conditional probability, i.e., random variables and expected values, and the probability of a given distribution converging to a certain value.
Journal ArticleDOI
C ONDENSATION —Conditional Density Propagation forVisual Tracking
Michael Isard,Andrew Blake +1 more
TL;DR: The Condensation algorithm uses “factored sampling”, previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set.