Rathnayake Tharindu, Tennakoon Ruwan, Khodadadian Gostar Amirali, Bab-Hadiashar Alireza, Hoseinnezhad Reza
School of Engineering, RMIT University, Melbourne, VIC 3000, Australia.
Sensors (Basel). 2019 Apr 29;19(9):2016. doi: 10.3390/s19092016.
This paper presents a novel Track-Before-Detect (TBD) Labeled Multi-Bernoulli (LMB) filter tailored for industrial mobile platform safety applications. At the core of the developed solution is two techniques for fusion of color and edge information in visual tracking. We derive an application specific separable likelihood function that captures the geometric shape of the human targets wearing safety vests. We use a novel geometric shape likelihood along with a color likelihood to devise two Bayesian updates steps which fuse shape and color related information. One approach is sequential and the other is based on weighted Kullback-Leibler average (KLA). Experimental results show that the KLA based fusion variant of the proposed algorithm outperforms both the sequential update based variant and a state-of-art method in terms of the performance metrics commonly used in computer vision literature.
本文提出了一种专为工业移动平台安全应用量身定制的新型先跟踪后检测（TBD）标记多伯努利（LMB）滤波器。所开发解决方案的核心是视觉跟踪中颜色和边缘信息融合的两种技术。我们推导了一个特定于应用的可分离似然函数，该函数捕捉了穿着安全背心的人体目标的几何形状。我们使用一种新颖的几何形状似然以及颜色似然来设计两个贝叶斯更新步骤，以融合形状和颜色相关信息。一种方法是顺序的，另一种基于加权库尔贝克 - 莱布勒平均（KLA）。实验结果表明，在所提出算法的基于KLA的融合变体在计算机视觉文献中常用的性能指标方面优于基于顺序更新的变体和一种现有方法。
