An optimized video synopsis algorithm and its distributed processing model

Video synopsis is one of the popular research topics in the field of digital video and has broad application prospects. Current research of it focuses on the methods of generating video synopsis or studying to utilize optimization algorithms such as fuzzy theory, minimum sparse reconstruction, and genetic algorithm to optimize its computing steps. This paper mainly studies the object-based video synopsis technology in distributed environment. We propose an effective video synopsis algorithm and a distributed processing model to accelerate the computing speed of video synopsis. The algorithm is proposed for studies of surveillance videos, which focuses on several key algorithmic steps, for instance, initialization of original video resources, background modeling, moving object detecting, and nonlinear rearrangement. These steps can be performed in parallel. In order to obtain good video synopsis effect and fast computing speed, some optimization methods are applied to these steps. With the aim of employing much more computing resources, we propose a distributed processing model, which splits the original video file into multiple segments and distributes them to different computing nodes to improve the computing performance by leveraging the multi-core and multi-thread capabilities of CPU. Experimental results show that the proposed distributed model can significantly improve the computing speed of video synopsis.