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.
翻译:物体探测和物体跟踪通常被视为两个不同的过程。在利用深层学习网络对2D图像进行物体探测方面已经取得重大进展。通常的物体跟踪跟踪跟踪跟踪管道要求在第一个框架和随后所有框架中成功探测到物体,并且通过将探测结果联系起来进行跟踪。通过单一网络进行物体探测和物体跟踪仍然是一个具有挑战性的问题。我们提议建立一个名为跟踪网络的新网络结构,通过扩展快速的R-CNN框架,直接探测在视频段内含有移动物体的3D管。提议在轨网内建立一个Tube 提议网络,以预测指定捆绑管的每个候选管子和位置参数的物品性质。拟议的框架适用于探测和跟踪任何物体,在本文件中,我们侧重于将其应用于交通视频分析。拟议的模型是用UA-DETRAC来培训和测试的,这是一个可供多车辆探测和跟踪使用的大型交通视频数据集,并获得了非常有希望的结果。