基于图形处理器的视频二值概率分割

针对现有视频二值分割算法分割性能过低的问题,提出了一种基于GPU的视频实时二值概率分割算法.该算法通过规范化视频帧中每个像素属于前景类和背景类的概率大小,实现了基于二次马尔可夫测量场(QMMF)模型的视频实时二值概率分割.首先分别为不同场景的视频帧提出了两种概率模型,即静态背景概率模型(SBLM)和动态背景概率模型(UBLM);然后,通过光照矫正算法颜色转换、阴影抑制算法阴影检测以及伪装检测算法来计算每个像素属于背景类的概率值;最后,通过Gauss-Seidel模型迭代计算出了使能量函数取得最小值的背景概率值进而得到像素的二值化值.此外,为了提高算法分割的准确性,该算法包含了对光照突变、投射阴影以及伪装情况的实时处理.同时,为了满足算法的实时性要求,在NVIDIA GPU上并行实现了该算法.验证了所提算法的分割性能即算法分割的正确性,测试了算法的GPU执行时间.实验结果表明,在算法分割完整性方面ViBe+和GMM+的平均漏检率和平均误检率分别是QMMF的3倍和6倍;在算法执行时间方面ViBe+和GMM+的平均GPU执行时间大约是QMMF的1.3倍.此外,还计算了QMMF算法的GPU加速比约为76.8.

Binary probability segmentation of video based on graphics processing unit

Since the segmentation performance of existing binary segmentation algorithm for video is excessively low, a binary probability segmentation algorithm in real-time based on Graphics Processing Unit (GPU) was proposed. The algorithm implemented a probabilistic segmentation based on the Quadratic Markov Measure Field (QMMF) model by regularizing the likelihood of each pixel of frame belonging to forground class or background class. In this algorithm, first two kinds of likelihood models, Static Background Likelihood Model (SBLM) and Unstable Background Likelihood Model (UBLM) were proposed. Secondly, the probability of each pixel belonging to background was computed by tonal transforming, cast shadow detecting and camouflage detecting algorithm. Finally, the probability of background which makes the energy function have a minimum value was computed by Gauss-Seidel model iteration and the binary value of each pixel was calculated. Moreover, illumination change, cast shadow and camouflage were included to improve the accuracy of segmentation algorithm. In order to fulfill the real-time requirement, a parallel version of our algorithm was implemented in a NVIDIA GPU. The accuracy and GPU execution time of the segmentation algorithm were analyzed. The experimental results show that the average missing rate and false detection rate of ViBe+ and GMM+ are 3 and 6 times those of QMMF, the average execution time of GPU of ViBe+ and GMM+ is about 1.3 times that of QMMF. Moreover, the average speedup of algorithm was computed and it is about 76.8.