Application of spatiotemporal search-space reduction in license plate detection

Abstract

In surveillance applications, search space reduction (SSR) is an essential element to efficient algorithms. In this study, spatial and temporal SSRs are integrated for license plate detection in video sequences; the plates could be extracted robustly and extremely fast. Our method started from spatial SSR by a bi-level one-pass plate extraction (BOPE) algorithm developed to extract plates accurately even in complicated situations. Moreover, we proposed to exclude repeated patterns with the similar appearances in the same location of consecutive frames, which usually include stopped vehicles or regular backgrounds. For efficiency, repeated patterns were detected only on the results of BOPE, named spatiotemporal SSR, based on a block-based mechanism by estimating the tangent distance, which is invariant to the variations in positions, sizes, rotations, or brightness. To reduce the computational load, the repeated patterns and their measured invariant features could be retained for next estimation. In our experiments, the search space could be reduced up to 87.9% by the spatiotemporal SSR; all plate numbers with the heights ranging from 24 to 40 are extracted correctly. The high performance, average 76 frames per second on a 3G Hz PC, suggests that the real-time goal for surveillance applications could be accomplished.