Abstract
This paper presents a parallel TBB-CUDA implementation for the acceleration of single-Gaussian distribution model, which is effective for background removal in the video-based fire detection system. In this framework, TBB mainly deals with initializing work of the estimated Gaussian model running on CPU, and CUDA performs background removal and adaption of the model running on GPU. This implementation can exploit the combined computation power of TBB-CUDA, which can be applied to the real-time environment. Over 220 video sequences are utilized in the experiments. The experimental results illustrate that TBB+CUDA can achieve a higher speedup than both TBB and CUDA. The proposed framework can effectively overcome the disadvantages of limited memory bandwidth and few execution units of CPU, and it reduces data transfer latency and memory latency between CPU and GPU.
Highlights
Video-based fire detection systems play an important role in the existing surveillance systems
This paper presents a parallel TBB-CUDA implementation for the acceleration of single-Gaussian distribution model, which is effective for background removal in the video-based fire detection system
We apply TBB to initialize work running on CPU, and CUDA to perform background removal and adaption of model running on GPU
Summary
Video-based fire detection systems play an important role in the existing surveillance systems. In addition to ordinary motion and color clues, flame and fire flickers can be detected by analyzing the video in wavelet domain [5,6,7] These methods have been successfully applied in surveillance systems and proven effective. CUDA (Compute Unified Device Architecture), created by NVIDIA [8], provides a data-parallel programming framework and enables parallel execution of C function kernels [9] For this reason, many developers have taken advantage of the high performance of CUDA to accelerate computation across various problem domains, such as signal processing, computer vision, computational geometry, and scientific computing [10,11,12,13]. We apply TBB to initialize work running on CPU, and CUDA to perform background removal and adaption of model running on GPU.
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