Three-level GPU accelerated Gaussian mixture model for background subtraction

Gaussian Mixture Model (GMM) for background subtraction (BGS) is widely used for detecting and tracking objects in video sequences. Although the GMM can provide good results, low processing speed has become its bottleneck for realtime applications. We propose a novel method to accelerate the GMM algorithm based on graphics processing unit (GPU). As GPU excels at performing massively parallel operations, the novelty lies in how to adopt various optimization strategies to fully exploit GPU's resources. The parallel design consists of three levels. On the basis of first-level implementation, we employ techniques such as memory access coalescing and memory address saving to the secondlevel optimization and the third-level modification, which reduces the time cost and increases the bandwidth greatly. Experimental results demonstrate that the proposed method can yield performance gains of 145 frames per second (fps) for VGA (640*480) video and 505 fps for QVGA (320*240) video which outperform their CPU counterparts by 24X and 23X speedup respectively. The resulted surveillance system can process five VGA videos simultaneously with strong robustness and high efficiency.