Multitarget-Multisensor Tracking Applications and Advances Vol III[

The subject of this book is nite set statistics (FISST) recently developed theory that unies much of information fusion under a single probabilistic—in fact, Bayesian—paradigm.

介绍多目标跟踪算法，可用于毫米波雷达目标的跟踪处理。可以基于位置、速度、角度等原始目标数据，通过处理跟踪和预测目标的轨迹。 Abstract—Multitarget tracking (MTT) refers to the problem of jointly estimating the number of targets and their states or trajectories from noisy sensor measurements. MTT has a long history spanning over 50 years, with a plethora of applications in many fields of study. While numerous techniques have been developed, the three most widely used approaches to MTT are the joint probabilistic data association filter (JPDAF), multiple hypothesis tracking (MHT), and random finite set (RFS). The JPDAF and MHT have been widely used for more than two decades, while the random finite set (RFS) based MTT algorithms have received a great deal of attention during the last decade. In this article, we provide an overview of MTT and succinct summaries of popular state-of-the-art MTT algorithms.

Multitarget-Multisensor Tracking Applications and Advances Vol III[2000]

本文当为经典的随机有限集文档，出自ronald mahler博士

最后更改 新许可证Apache 2.0代替GPLv3 添加了新的参数“批处理大小”-在多个连续帧上同时检测。 它可以在功能强大的GPU上提高处理速度。 适用于Darknet和TensorRT后端，但可能会增加一些延迟 新影片！ 使用YOLO v4进行车速计算（感谢 ） YOLO v4迈向ADAS的第一步 多目标（多个对象）跟踪器 1.可以使用具有不同的detectorType值的功能创建对象检测器： 1.1。 根据背景扣除：内置Vibe（跟踪：: Motion_VIBE），SuBSENSE（跟踪：: Motion_SuBSENSE）和LOBSTER（跟踪：: Motion_LOBSTER）； 来自MOG2（tracking :: Motion_MOG2）； MOG（跟踪:: Motion_MOG），GMG（跟踪:: Motion_GMG），并从CNT（跟踪:: Motion_CNT