基于GPU_CPU异构并行加速的人头检测方法

多尺度协同的人头检测系统中，梯度方向直方图应用于高清视频监控领域时常因特征提取时的海量计算而不能满足监控视频的实时性要求，提出一种基于GPU_CPU异构并行加速的人头检测方法，GPU端负责HOG特征提取的庞大的密集型的区块的并行计算，CPU端负责检测过程中的其它模块的执行.传统的并行归约算法因其在HOG特征提取过程中的时间复杂度不够理想，提出改进的并行归约算法，通过“下扫”的并行计算方式，减少节点被计算的次数，降低了HOG特征提取时的时间复杂度.实验表明，提出的方法检测速率优于传统的CPU的检测方法，其效率提升约10倍.

Human Head Detection Based on GPU_CPU Heterogeneous Parallel Acceleration

In the multi-scale collaborative human head detection system, the gradient direction histogram cannot meet the real-time requirement of video surveillance because of the massive computation in the high-definition video surveillance field. This paper proposes a method of human head detection based on GPU_CPU heterogeneous parallel acceleration. The GPU is responsible for the HOG feature extraction of large-intensive block parallel computing, and CPU is responsible for the implementation of other modules. The traditional parallel reduction algorithm is not excellent in the HOG feature extraction, and an improved parallel reduction algorithm is therefore proposed, which reduces the time complexity by the parallel computing of down-sweep to reduce calculated times of nodes, and the experimental results show that the proposed method is more efficient than the traditional one for over about 10 times.