Adaptive mixture observation models for multiple object tracking
β Scribed by Peng Cui; LiFeng Sun; ShiQiang Yang
- Book ID
- 107357478
- Publisher
- Science in China Press (SCP)
- Year
- 2009
- Tongue
- English
- Weight
- 625 KB
- Volume
- 52
- Category
- Article
- ISSN
- 1674-733X
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
In this letter, we present a novel dynamical Gaussian mixture model (DGMM) for tracking elliptical living objects in video frames. The parameters, which inform object position and shape, are estimated by using a traditional Gaussian mixture model (GMM) for the first frame. Instead of simply using th
In this paper, we propose a model-based tracking algorithm which can extract trajectory information of a target object by detecting and tracking a moving object from a sequence of images. The algorithm constructs a model from the detected moving object and match the model with successive image frame
Heterogeneity in biomedical data is often a source of great scientific interest and mixture models provide a general framework for modelling the various types that arise in practice. Finite mixture models model discrete subgroups within populations while continuous mixture models inflate the varianc