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Paper WeA09.5

Vidal-Calleja, Teresa A. (University of Sydney), Agamennoni, Gabriel (The University of Sydney)

Integrated Probabilistic Generative Model for Detecting Smoke on Visual Images

Scheduled for presentation during the Regular Session "Surveillance" (WeA09), Wednesday, May 16, 2012, 09:30−09:45, Meeting Room 9 (Sa)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on June 19, 2018

Keywords Surveillance Systems, Recognition

Abstract

Early fire detection is crucial to minimise damage and save lives. Video surveillance smoke detectors do not suffer from transport delays and can cover large areas. The smoke detection on images is, however, a difficult problem due the variability of smoke density, lighting conditions, background clutter, and unstable patterns. In order to solve this problem, we propose a novel unsupervised object classifier. Single visual features are classified using a model that simultaneously creates a codebook and categorises the smoke using a bag-of-words paradigm based on LDA model. Our algorithm can also tell the amount of smoke present on the image. Multiple image sequences from different cameras are used to show the viability of the proposed approach. Our experiments show that the model generalises well for different cameras, perspectives and scales.

 

 

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