Visual Smoke Detection Using Low-Cost Surveillance Cameras

Researchers from the University of Ulsan in Korea have demonstrated how to spot fires before they spread by using GPU-accelerated video processing to detect smoke, eliminating the need for expensive infra-red cameras and laser range finders.

Real-Time Smoke Detection for Surveillance_2
Smoke detection false positives: (a) Original image without reflection detected; (b) Result image without reflection detected; (c) Original image with reflection detected; (d) Result image with reflection detected.

Surveillance tasks include not only human detection and behavior recognition but also understanding of a current state of the environment. When fire occurs in the scene, it usually occupies a small area, but produces big amount of smoke. If this kind of scenarios occurs in a forest, then flames can spread far before they become visible form a distance. At the same time, burning trees produce noticeable volumes of smoke. Thus, smoke plays the role of early alarm starter when there are no flames visible yet.

The paper introduces the smoke detection method for surveillance cameras. The background subtraction was used to determine moving objects. Color probability was utilized to find possible smoke pixels in a scene. Separate pixels, acquired by background subtraction, were united by morphological operations and connected components labeling methods. The existence of the smoke region is then confirmed by boundary roughness and edge density. In the last step, the current frame is compared to the previous one in order to check the behavior of objects. The most computationally expensive steps are processed in parallel using CUDA to achieve real-time performance. Computational time was decreased by more than 6 times comparing to the CPU processing only – using an NVIDIA GeForce GPU.

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About Brad Nemire

Brad Nemire
Brad Nemire is on the Developer Marketing team and loves reading about all of the fascinating research being done by developers using NVIDIA GPUs. Reach out to Brad on Twitter @BradNemire and let him know how you’re using GPUs to accelerate your research. Brad graduated from San Diego State University and currently resides in San Jose, CA. Follow @BradNemire on Twitter