Tracking objects using 3D object proposals

TL;DR

This paper introduces an online method for tracking static objects in RGB-D video sequences using 3D object proposals and shape matching, emphasizing the importance of depth information for improved accuracy and efficiency.

Contribution

It presents a novel real-time approach that leverages 3D object proposals and depth data for object tracking, suitable for low-power embedded systems.

Findings

Runs in less than a second on MATLAB

Effective in low-power UAVs and drones

Utilizes depth information for better shape matching

Abstract

3D object proposals, quickly detected regions in a 3D scene that likely contain an object of interest, are an effective approach to improve the computational efficiency and accuracy of the object detection framework. In this work, we propose a novel online method that uses our previously developed 3D object proposals, in a RGB-D video sequence, to match and track static objects in the scene using shape matching. Our main observation is that depth images provide important information about the geometry of the scene that is often ignored in object matching techniques. Our method takes less than a second in MATLAB on the UW-RGBD scene dataset on a single thread CPU and thus, has potential to be used in low-power chips in Unmanned Aerial Vehicles (UAVs), quadcopters, and drones.