| 基于嵌入式GPU的运动目标分割算法并行优化 |
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| 引用本文： | 张刚,马震环,雷涛,崔毅,张三喜. 基于嵌入式GPU的运动目标分割算法并行优化[J]. 应用光学, 2019, 40(6): 1067-1076. DOI: 10.5768/JAO201940.0602004 |
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| 作者姓名： | 张刚 马震环 雷涛 崔毅 张三喜 |
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| 作者单位： | 1.中国科学院大学，北京100049 |
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| 基金项目： | 中科院青年创新促进会基金2016336 |
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| 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。
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| 关 键 词： | 运动目标分割 并行优化 PBAS GPU |
| 收稿时间： | 2019-06-10 |
Embedded GPU-based parallel optimization for moving objects segmentation algorithm |
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| Affiliation: | 1.University of Chinese Academy of Sciences, Beijing 100049, China2.Institute of Optics and Electronics, CAS, Chengdu 610209, China3.China Huayin Ordnance Test Center, Huayin 714200, China |
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| Abstract: | 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. |
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