Refined Particle Swarm Intelligence Method for Abrupt Motion Tracking
This work addresses abrupt motion tracking for video surveillance and robotics, but it is incremental as it builds on existing swarm intelligence methods.
The paper tackled abrupt motion tracking by proposing SwaTrack, a novel tracker based on particle swarm optimization with an optimized sampling strategy and dynamic acceleration parameters, achieving effective tracking results in both quantitative and qualitative experiments.
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.