Mice and larvae tracking using a particle filter with an auto-adjustable observation model
✍ Scribed by Hemerson Pistori; Valguima Victoria Viana Aguiar Odakura; João Bosco Oliveira Monteiro; Wesley Nunes Gonçalves; Antonia Railda Roel; Jonathan de Andrade Silva; Bruno Brandoli Machado
- Publisher
- Elsevier Science
- Year
- 2010
- Tongue
- English
- Weight
- 830 KB
- Volume
- 31
- Category
- Article
- ISSN
- 0167-8655
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✦ Synopsis
This paper proposes a novel way to combine different observation models in a particle filter framework. This, so called, auto-adjustable observation model, enhance the particle filter accuracy when the tracked objects overlap without infringing a great runtime penalty to the whole tracking system. The approach has been tested under two important real world situations related to animal behavior: mice and larvae tracking. The proposal was compared to some state-of-art approaches and the results show, under the datasets tested, that a good trade-off between accuracy and runtime can be achieved using an autoadjustable observation model.