Refined Particle Swarm Intelligence Method for Abrupt Motion Tracking

TL;DR

This paper introduces SwaTrack, a novel swarm intelligence-based tracking method designed to effectively handle abrupt motions by optimizing sampling strategies and dynamically tuning parameters, outperforming traditional smooth-motion trackers.

Contribution

The paper presents a new abrupt motion tracker using swarm intelligence with optimized sampling and dynamic parameter tuning, a novel approach not previously explored.

Findings

Effective in tracking abrupt motions in experiments

Outperforms existing methods in accuracy and robustness

Demonstrates versatility across different scenarios

Abstract

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.