School of Electronic and Information Engineering, Beihang University, Beijing 100191, China.
Sensors (Basel). 2018 Nov 14;18(11):3944. doi: 10.3390/s18113944.
The joint detection and tracking of multiple targets from raw thermal infrared (TIR) image observations plays a significant role in the video surveillance field, and it has extensive applied foreground and practical value. In this paper, a novel multiple-target track-before-detect (TBD) method, which is based on background subtraction within the framework of labeled random finite sets (RFS) is presented. First, a background subtraction method based on a random selection strategy is exploited to obtain the foreground probability map from a TIR sequence. Second, in the foreground probability map, the probability of each pixel belonging to a target is calculated by non-overlapping multi-target likelihood. Finally, a δ generalized labeled multi-Bernoulli ( δ -GLMB) filter is employed to produce the states of multi-target along with their labels. Unlike other RFS-based filters, the proposed approach describes the target state by a pixel set instead of a single point. To meet the requirement of factual application, some extra procedures, including pixel sampling and update, target merging and splitting, and new birth target initialization, are incorporated into the algorithm. The experimental results show that the proposed method performs better in multi-target detection than six compared methods. Also, the method is effective for the continuous tracking of multi-targets.
从原始热红外（TIR）图像观测中联合检测和跟踪多个目标在视频监控领域中具有重要作用，具有广泛的应用前景和实用价值。本文提出了一种新的基于标记随机有限集（RFS）框架内背景减除的多目标跟踪前检测（TBD）方法。首先，利用基于随机选择策略的背景减除方法从 TIR 序列中获得前景点概率图。其次，在前景点概率图中，通过非重叠多目标似然计算每个像素属于目标的概率。最后，采用 δ 广义标记多伯努利（δ-GLMB）滤波器生成多目标及其标签的状态。与其他基于 RFS 的滤波器不同，所提出的方法通过像素集而不是单个点来描述目标状态。为了满足实际应用的要求，该算法中还包含了一些额外的步骤，包括像素采样和更新、目标合并和分裂，以及新出生目标的初始化。实验结果表明，与其他六种比较方法相比，所提出的方法在多目标检测方面表现更好。此外，该方法还可以有效地对多目标进行连续跟踪。
