论文标题
TractNet：用于多目标多摄像机车辆跟踪的基于三重态度量的方法
TrackNet: A Triplet metric-based method for Multi-Target Multi-Camera Vehicle Tracking
论文作者
论文摘要
我们提出了TrackNet，这是一种来自交通视频序列的多目标多摄像机（MTMC）车辆跟踪的方法。跨相机车辆跟踪已被证明是一项艰巨的任务，这是由于透视，规模和速度差异以及遮挡和噪声条件。我们的方法基于一种模块化方法，该方法首先使用更快的R-CNN检测车辆逐帧，然后使用Kalman滤波器通过单个相机跟踪检测，最后通过三重态度量学习策略匹配轨道。我们在AI City Challenge框架内的踪迹上进行实验，并提出竞争性IDF1结果为0.4733。
We present TrackNet, a method for Multi-Target Multi-Camera (MTMC) vehicle tracking from traffic video sequences. Cross-camera vehicle tracking has proved to be a challenging task due to perspective, scale and speed variance, as well occlusions and noise conditions. Our method is based on a modular approach that first detects vehicles frame-by-frame using Faster R-CNN, then tracks detections through single camera using Kalman filter, and finally matches tracks by a triplet metric learning strategy. We conduct experiments on TrackNet within the AI City Challenge framework, and present competitive IDF1 results of 0.4733.
