Refined Particle Swarm Intelligence Method for Abrupt Motion Tracking

• Published in: arXiv.org
• Main Authors: Lim, Mei Kuan, Chan, Chee Seng, Monekosso, Dorothy, Remagnino, Paolo
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 14.10.2014
• Subjects: Acceleration; Computer Science - Computer Vision and Pattern Recognition; Computer Science - Neural and Evolutionary Computing; Optimization; Sampling; Smoothness; Swarm intelligence; Tracking
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1410.3744

Conventional tracking solutions are not feasible in handling abrupt motion as they are based on smooth motion assumption or an accurate motion model. Abrupt motion is not subject to motion continuity and smoothness. To assuage this, we deem tracking as an optimisation problem and propose a novel abrupt motion tracker that based on swarm intelligence - the SwaTrack. Unlike existing swarm-based filtering methods, we first of all introduce an optimised swarm-based sampling strategy to tradeoff between the exploration and exploitation of the search space in search for the optimal proposal distribution. Secondly, we propose Dynamic Acceleration Parameters (DAP) allow on the fly tuning of the best mean and variance of the distribution for sampling. Such innovating idea of combining these strategies in an ingenious way in the PSO framework to handle the abrupt motion, which so far no existing works are found. Experimental results in both quantitative and qualitative had shown the effectiveness of the proposed method in tracking abrupt motions.