摘要
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。
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.
作者
张刚
马震环
雷涛
崔毅
张三喜
ZHANG Gang;MA Zhenhuan;LEI Tao;CUI Yi;ZHANG Sanxi(University of Chinese Academy of Sciences,Beijing 100049,China;Institute of Optics and Electronics,CAS,Chengdu 610209,China;China Huayin Ordnance Test Center,Huayin 714200,China)
出处
《应用光学》
CAS
CSCD
北大核心
2019年第6期1067-1076,共10页
Journal of Applied Optics
基金
中科院青年创新促进会基金(2016336)
