论文标题
具有自适应广义标记的多伯努利滤波器的多对象跟踪
Multi-object Tracking with an Adaptive Generalized Labeled Multi-Bernoulli Filter
论文作者
论文摘要
多对象跟踪的挑战主要源于跟踪过程中基数和对象状态的随机变化。此外，有关对象出现的位置，其检测概率以及传感器错误警报的统计信息的信息显着影响过滤器的跟踪精度。但是，通常认为此信息是用户已知和提供的。在本文中，我们提出了一个自适应的广义标记的多伯努利（GLMB）滤波器，该过滤器可以在不知道上述信息的情况下跟踪多个对象。实验结果表明，所提出的过滤器的性能与提供的理想GLMB过滤器相媲美，并提供了跟踪方案的正确信息。
The challenges in multi-object tracking mainly stem from the random variations in the cardinality and states of objects during the tracking process. Further, the information on locations where the objects appear, their detection probabilities, and the statistics of the sensor's false alarms significantly influence the tracking accuracy of the filter. However, this information is usually assumed to be known and provided by the users. In this paper, we propose an adaptive generalized labeled multi-Bernoulli (GLMB) filter which can track multiple objects without prior knowledge of the aforementioned information. Experimental results show that the performance of the proposed filter is comparable to an ideal GLMB filter supplied with correct information of the tracking scenarios.
