Tracking objects using 3D object proposals
This work addresses object tracking for applications in low-power devices like UAVs and drones, but it is incremental as it builds on existing 3D object proposals.
The paper tackles the problem of tracking static objects in RGB-D video sequences by using 3D object proposals and shape matching, achieving processing times of less than a second on a single-thread CPU.
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.