RSMOT: Remote Sensing Multi-Object Tracking Network with Local Motion Prior for Objects in Satellite Videos

Multi-object tracking (MOT) in satellite videos is a new and challenging task. The difficulties stem from the extremely small objects and the low contrast between objects and background. To tackle the challenges of MOT in satellite videos, a multi-object tracking method is proposed in this paper to incorporate the local motion prior into the network. Specifically, we design a local cost volume construction module to obtain tracking offsets between adjacent frames. Based on the tracking offsets, features of previous frames can be propagated to the current frame to incorporate spatio-temporal information. We conduct extensive experiments on videos from Jilin-1 satellite, and the results demonstrate the effectiveness of the proposed method.

1. Introduction

Multiple object tracking (MOT) has been a central topic in computer vision and plays an important role in robotics, surveillance, and autonomous driving [1], [2]. MOT aims to locate the objects of interests in each frame and associate them to generate trajectories across video frames. MOT in satellite videos is a new and challenging task [3]. The main challenges stem from the following aspects, as shown in Fig. 1.