A Spatio-Temporal Probabilistic Model for Multi-Sensor Multi-Class Object Recognition

This paper presents a general probabilistic framework for multi-sensor multi-class object recognition based on Conditional Random Fields (CRFs) trained with virtual evidence boosting. The learnt representation models spatial and temporal relationships and is able to integrate arbitrary sensor information by automatically extracting features from data. We demonstrate the benefits of modelling spatial and temporal relationships for the problem of detecting seven classes of objects using laser and vision data in outdoor environments. Additionally, we show how this framework can be used with partially labeled data, thereby significantly reducing the burden of manual data annotation.

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