基于嵌入式GPU的运动目标分割算法并行优化

在光电监视系统中，广泛应用于运动目标分割的PBAS(pixel base adaptive segmenter)算法计算复杂、参数量大，难以达到实时分割的要求。针对PBAS算法是对图像中每个像素点进行独立处理，特别适合于GPU并行加速的特点，对其在嵌入式GPU平台Jetson TX2上进行了并行优化实现。在数据存储结构、共享内存使用、随机数产生机制3个方面对该算法进行了优化设计。实验结果表明，对于480×320像素分辨率的中波红外视频序列，该并行优化方法可以达到132 fps的处理速度，满足了实时处理的要求。

Embedded GPU-based parallel optimization for moving objects segmentation algorithm

In optoelectronic surveillance systems, the pixel base adaptive segmenter (PBAS) algorithm, which is widely used in moving objects segmentation, is hard to meet the requirements of real-time applications due to its calculating complication and a large amount of computing parameters. With its pixel-level parallelism, deploying PBAS on top of graphic processing unit (GPU) is promising. This paper implements real-time optimization of PBAS on embedded GPU platform-Jetson TX2, employing methods of data storage architecture, shared memory utilization and random number generation. Experimental results show that the parallel optimization method can achieve 132 fps when processing 480×320 pixel medium-wave infrared video sequences, thus meets the real-time processing need.