Trajectory Factory：通过Deep Siamese Bi-GRU进行Tracklet切割和重新连接，用于多目标跟踪
Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking
摘要：
多目标跟踪（MOT）在监控、自动驾驶等复杂场景中是一项具有挑战性的任务。在本文中，我们提出了一种新的tracklet处理方法，切割和重新连接tracklet人群或长期闭塞的暹罗双门递归单元（GRU）。tracklet生成利用由CNN和RNN提取的对象特征来创建稀疏场景中的高置信度tracklet候选。由于生成过程中的误跟踪，来自不同对象的轨迹被双向GRU分成几个子轨迹。在此基础上，采用基于Siamese GRU的轨迹重连接方法，将属于同一对象的子轨迹连接起来，形成一个完整的轨迹。此外，我们从现有的MOT数据集中提取tracklet图像，并提出了一个新的数据集来训练我们的网络。建议的数据集包含超过95160个行人图像。它有793个不同的人。平均来说，每个人有120张照片，有位置和大小。实验结果表明，我们的模型的优势，在MOT 16的最先进的方法。
英文摘要：
Multi-Object Tracking (MOT) is a challenging task in the complex scene such as surveillance and autonomous driving. In this paper, we propose a novel tracklet processing method to cleave and re-connect tracklets on crowd or long-term occlusion by Siamese Bi-Gated Recurrent Unit (GRU). The tracklet generation utilizes object features extracted by CNN and RNN to create the high-confidence tracklet candidates in sparse scenario. Due to mis-tracking in the generation process, the tracklets from different objects are split into several sub-tracklets by a bidirectional GRU. After that, a Siamese GRU based tracklet re-connection method is applied to link the sub-tracklets which belong to the same object to form a whole trajectory. In addition, we extract the tracklet images from existing MOT datasets and propose a novel dataset to train our networks. The proposed dataset contains more than 95160 pedestrian images. It has 793 different persons in it. On average, there are 120 images for each person with positions and sizes. Experimental results demonstrate the advantages of our model over the state-of-the-art methods on MOT16.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
