Complex algorithm optimization through probabilistic search of its configuration tree

We present a novel algorithm to automatically configure complex video processing systems when adapting them to new datasets or scenarios. This has the main benefit of significantly reducing the time spent by a system expert on parameter tuning. Our approach has two main components: (1) a configuration tree structure that organizes system parameters and procedures in a systematic manner and facilitates identifying viable system configurations; and (2) a probabilistic sampling approach on the configuration tree that efficiently searches for the optimal configuration. We have successfully applied the proposed approach to optimize the configurations for two different video processing modules: a motion detection & tracking pipeline and a streaming video segmentation algorithm. The automatically discovered configurations produced better performance than the best manual configurations, while requiring significantly less human effort and domain-specific expertise.