  Panoramic video is a sort of video recorded at the same point of view to
record the full scene. With the development of video surveillance and the
requirement for 3D converged video surveillance in smart cities, CPU and GPU
are required to possess strong processing abilities to make panoramic video.
The traditional panoramic products depend on post processing, which results in
high power consumption, low stability and unsatisfying performance in real
time. In order to solve these problems,we propose a real-time panoramic video
stitching framework.The framework we propose mainly consists of three
algorithms, LORB image feature extraction algorithm, feature point matching
algorithm based on LSH and GPU parallel video stitching algorithm based on
CUDA.The experiment results show that the algorithm mentioned can improve the
performance in the stages of feature extraction of images stitching and
matching, the running speed of which is 11 times than that of the traditional
ORB algorithm and 639 times than that of the traditional SIFT algorithm. Based
on analyzing the GPU resources occupancy rate of each resolution image
stitching, we further propose a stream parallel strategy to maximize the
utilization of GPU resources. Compared with the L-ORB algorithm, the efficiency
of this strategy is improved by 1.6-2.5 times, and it can make full use of GPU
resources. The performance of the system accomplished in the paper is 29.2
times than that of the former embedded one, while the power dissipation is
reduced to 10W.
