TrackNet：同时目标检测和跟踪及其在交通视频分析中的应用
TrackNet: Simultaneous Object Detection and Tracking and Its Application in Traffic Video Analysis
摘要：
目标检测和目标跟踪通常被视为两个独立的过程。使用深度学习网络在2D图像中的目标检测方面取得了重大进展。用于对象跟踪的通常的逐检测跟踪流水线要求在第一帧和所有后续帧中成功检测对象，并且通过关联检测结果来完成跟踪。通过单个网络执行目标检测和目标跟踪仍然是一个具有挑战性的开放问题。我们提出了一种名为trackNet的新型网络结构，通过扩展更快的R-CNN框架，可以直接检测视频片段中包含移动对象的3D管。trackNet内部的Tube Proposal Network（TPN）用于预测每个候选管道的对象和指定边界管道的位置参数。所提出的框架适用于检测和跟踪任何对象，在本文中，我们专注于它的应用交通视频分析。该模型在UA-DETRAC（一个可用于多车辆检测和跟踪的大型交通视频数据集）上进行了训练和测试，并获得了非常有前途的结果。
英文摘要：
Object detection and object tracking are usually treated as two separate processes. Significant progress has been made for object detection in 2D images using deep learning networks. The usual tracking-by-detection pipeline for object tracking requires that the object is successfully detected in the first frame and all subsequent frames, and tracking is done by associating detection results. Performing object detection and object tracking through a single network remains a challenging open question. We propose a novel network structure named trackNet that can directly detect a 3D tube enclosing a moving object in a video segment by extending the faster R-CNN framework. A Tube Proposal Network (TPN) inside the trackNet is proposed to predict the objectness of each candidate tube and location parameters specifying the bounding tube. The proposed framework is applicable for detecting and tracking any object and in this paper, we focus on its application for traffic video analysis. The proposed model is trained and tested on UA-DETRAC, a large traffic video dataset available for multi-vehicle detection and tracking, and obtained very promising results.
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。
