GPGPU implementation of visual tracking by particle filter with pixel ratio likelihood
GPGPU implementation of visual tracking by particle filter with pixel ratio likelihood
复制标题
DOI：
10.1109/sii.2012.6427354
复制
发表时间：
2012-12
期刊：
影响因子：
--
通讯作者：
N. Ikoma;Takashi Ito
中科院分区：
文献类型：
--
作者：
N. Ikoma;Takashi Ito
利用感兴趣区域像素比计算似然的粒子滤波视觉跟踪算法虽然简单，但有着广泛的应用。本文提出了一种基于图形处理器的通用计算（GPGPU）并行计算视觉跟踪的实现方法。跟踪器的算法已经在CUDA框架中基本实现。该算法与完整算法的不同之处在于从原始图像中减小图像大小，以便在GPU硬件的有限大小的恒定存储器中处理多个图像进行似然计算。所提出的方法的性能达到30帧每秒（帧每秒）的特定彩色对象跟踪任务和超过10帧每秒的任务的手跟踪的汽车驾驶员操作转向。
Visual tracking by particle filter with pixel ratio in a region of interest for likelihood computation has wide range of applications despite of its simple algorithm. A GPGPU (General Purpose computation on Graphics Processing Unit) implementation of the visual tracking in parallel computation has been proposed in this paper. Algorithm of the tracker has almost fully been implemented in CUDA framework. Difference of the proposed algorithm from the full algorithm is a reduction of image size from the original image in order to deal with multiple images for likelihood computation in limited size of constant memory of the GPU hardware. Performance of the proposed method achieves 30 fps (frame per second) for specific colored object tracing task and more than ten frame per second for a task of hands tracking of a car driver operating a steering.