We propose a novel online multi-object visual tracker using a Gaussian
mixture Probability Hypothesis Density (GM-PHD) filter and deep appearance
learning. The GM-PHD filter has a linear complexity with the number of objects
and observations while estimating the states and cardinality of time-varying
number of objects, however, it is susceptible to miss-detections and does not
include the identity of objects. We use visual-spatio-temporal information
obtained from object bounding boxes and deeply learned appearance
representations to perform estimates-to-tracks data association for target
labeling as well as formulate an augmented likelihood and then integrate into
the update step of the GM-PHD filter. We also employ additional unassigned
tracks prediction after the data association step to overcome the
susceptibility of the GM-PHD filter towards miss-detections caused by
occlusion. Extensive evaluations on MOT16, MOT17 and HiEve benchmark datasets
show that our tracker significantly outperforms several state-of-the-art
trackers in terms of tracking accuracy and identification.

本文提出了一种新颖的在线多物体视觉跟踪器，它使用高斯混合概率假设密度 (GM-PHD) 滤波器和深度外观学习，并在数据关联后继续采用未分配轨迹预测以克服 GM-PHD 滤波器对遮挡引起的漏检敏感性，其在 MOT16，MOT17 和 HiEve 基准数据集上表现优于现有几种现有的跟踪器。