Fast Vanishing Point Estimation Based on Particle Swarm Optimization

Abstract

Xun PAN†a) , Student Member, Wa SI †b) , and Harutoshi OGAI †c) , Nonmembers SUMMARYVanishing point estimation is an important issue for vision based road detection, especially in unstructured roads. However, most of the existing methods suffer from the long calculating time. This paper focuses on improving the efficiency of vanishing point estimation by using a heuristic voting method based on particle swarm optimization (PSO). Experiments prove that with our proposed method, the efficiency of vanishing point estimation is significantly improved with almost no loss in accuracy. Moreover, for sequenced images, this method is further improved and can get even better performance, by making full use of inter-frame information to optimize the performance of PSO. key words: vanishing point estimation, particle swarm optimization, interframe information

Vanishing point detection using the teaching learning‐based optimisation algorithm

“…Metaheuristics have delivered better solutions in comparison with traditional methods. Regarding vanishing point estimation, VP detectors have been proposed using an artificial bee colony (ABC) [25], the particle swarm optimisation (PSO) [26], the binary PSO (BPSO) [27], and the harmony search (HS) algorithm [28]. However, when using these metaheuristics, the need of many parameters tuning remains.…”