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Guiding Random Samples by Deterministic Search
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2001 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Among the algorithms developed towards the goal of robust and efficient tracking, two approaches which stand out due to their success are those based on particle filtering and variational approaches. The Bayesian approach led to the development of the particle filter, which performs a random search guided by a stochastic motion model. On the other hand, localising an object can be based on minimising a cost function. This minimum can be found using variational methods. The search paradigms differ in these two methods. One is stochastic and model-driven while the other is deterministic and data-driven. This paper presents a new algorithm to incorporate the strengths of both approaches into one consistent framework. To allow this fusion a smooth, wide likelihood function is constructed, based on a sum-of-squares distance measure and an appropriate sampling scheme is introduced. Based on low-level information this scheme automatically mixes the two methods of search and adapts the computational demands of the algorithm to the difficulty of the problem at hand. The ability to effectively track complex motions without the need for finely tuned motion models is demonstrated

Place, publisher, year, edition, pages
2001. p. 323-330
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-69632DOI: 10.1109/ICCV.2001.937536OAI: oai:DiVA.org:kth-69632DiVA, id: diva2:485656
Conference
Proceedings of the International Conference on Computer Vision
Note
NR 20140805Available from: 2012-01-29 Created: 2012-01-29 Last updated: 2018-01-12Bibliographically approved

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