Abstract
Developing robust visual tracking algorithms for real-world application is a major challenge even today. In this chapter, we focus on visual tracking with data fusion using sequential Monte Carlo filtering techniques, and present a tracking framework with a four-layer probabilistic fusion. The framework consists of four layers: a cue fusion layer, a model fusion layer, a tracker fusion layer, and a sensor fusion layer, in a bottom-up fusion process. These layers are defined as follows: the cue layer fuses visual modalities via an adaptive fusion strategy, the model layer fuses prior motion information via an interactive multi-model method (IMM), the tracker layer fuses results from multiple trackers via an adaptive tracking mode hat switches between model associations, and the sensor layer fuses multiple sensors in a distributed way. Only state distributions in the input and output of each layer are required to ensure consistency of so many visual modules within the framework. Furthermore, the proposed framework is general and allows augmenting and pruning of fusion layers according to the visual environment at hand. The proposed framework is tested and satisfactory results obtained in various complex scenarios in which many existing trackers are inclined to fail.
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Zheng, N., Xue, J. (2009). Probabilistic Data Fusion for Robust Visual Tracking. In: Statistical Learning and Pattern Analysis for Image and Video Processing. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84882-312-9_9
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DOI: https://doi.org/10.1007/978-1-84882-312-9_9
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