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.