Privacy-Preserving Efficient Fire Detection System for Indoor Surveillance

Residential fire is a proven hazard for human life and property. Vision based approaches for fire detection are superior to sensor based ones in terms of accuracy and alleviating false positives. Several frameworks that utilize vision-based monitoring in combination with convolutional neural network and other machine learning algorithms, such as support vector machine, K-mean clustering, logistic regression, neural network, and decision rules are available in literature for fire detection. While such frameworks are effective, they cannot be used in private spaces such as inside homes and offices as the privacy of occupants is compromised. In this article, a vision based fire detection framework for monitoring private spaces while preserving the privacy of the occupant is proposed. This is a novel endeavor as no other approach has looked at the issue of privacy preservation in fire detection with vision sensors. The framework utilizes a near infra-red camera to capture images in a manner that the privacy of occupants is preserved. To confirm that images captured with this camera do preserve occupants' privacy, two random user surveys were conducted. For effective fire detection using these images, a novel system incorporating both spatial and temporal properties of fire is employed. Experiments were conducted and confirm the superiority of the proposed framework when compared with existing techniques in literature both in terms of performance and model size. In addition to this, the lightweight nature of the proposed system enables its effective use over resource-constrained environments as well. This is validated through a real-world prototypical implementation.

Automated summary

This research proposes a novel vision-based fire detection framework that preserves privacy by using a near infrared camera. The framework proves to have superior performance and model size compared to existing techniques. The effectiveness of the system in resource-constrained environments is validated through a real-world implementation.