作者：Jan Prokaj，G´erard Medioni，J Gradient

作者单位：University of Southern California（美国南加州大学）

发布时间：2014

发布期刊/会议：CVPR

论文全称：Persistent Tracking for Wide Area Aerial Surveillance

论文地址：

Persistent Tracking for Wide Area Aerial Surveillance

论文代码：

地位：

个人理解

• **创新点：**使用两个并行的跟踪器（一个基于背景减法，一个根据目标的外观进行建模）来实现对广域航空图像中目标的持续跟踪，无论目标运动还是停止。
• **为什么：**目前已有的在广域航空图像上跟踪任务基本都是基于背景减法（background subtraction）或帧差分（frame differencing）来实现的，但是一旦跟踪的对象运动缓慢或静止不动时，则几乎无法跟踪到该对象，无法实现持续跟踪。即已有的方法只能跟踪持续运动的对象，一旦对象静止不动，则会跟丢。
• **怎么做：**使用两个并行的跟踪器来对目标进行跟踪，再对这两个跟踪器的跟踪结果进行匹配，其中一个跟踪器是基于背景减法（background subtraction），用于检测在连续 5 帧内运动的对象，并输出运动对象的tracklet，而另一个跟踪器 Regression Tracker 则根据这些 tracklet 来对跟踪对象的外观进行建模，从而根据外观来进行跟踪，即跟踪对象静止不动也可被跟踪。

一、摘要

Persistent surveillance of large geographic areas from unmanned aerial vehicles allows us to learn much about the daily activities in the region of interest. Nearly all of the approaches addressing tracking in this imagery are detection based and rely on background subtraction or frame differencing to provide detections. This, however, makes it difficult to track targets once they slow down or stop, which is not acceptable for persistent tracking, our goal.

We present a multiple target tracking approach that does not exclusively rely on background subtraction and is better able to track targets through stops. It accomplishes this by effectively running two trackers in parallel: one based on detections from background subtraction providing target initialization and reacquisition, and one based on a target state regressor providing frame to frame tracking. We evaluated the proposed approach on a long sequence from a wide area aerial imagery dataset, and the results show improved object detection rates and ID-switch rates with limited increases in false alarms compared to the competition.

• 通过无人机对大区域的持续监测，可以深入了解感兴趣区域的日常活动。