Object Proposal Algorithms in the Wild: Are they Generalizable to Robot Perception?

The recent emergence of object proposal algorithms in the computer vision community shows great promise to addressing difficult problems in robotic such as object discovery and salient object detection. However, it is difficult to determine how these algorithms actually perform for real-world robot vision applications, because the standard evaluation protocol uses datasets which do not adequately account for real-world noise (motion blur, occlusion, etc.). We evaluated several state-of-the-art object proposal algorithms using naturalistic datasets from the robotics community, and found a substantial performance drop across all algorithms. This suggests that many object proposal algorithms are not as generalizable as the computer vision literature purports, which can have a significant impact on how they are applied to robotics. We also conducted a study on how each algorithm is influenced by specific kinds of real-world robot vision challenges, including variable brightness, gamma correction, Gaussian blur, and Gaussian noise. Our results provide insight into certain weaknesses of object proposal algorithms, which can be used to gauge how they might be suitable for different robotics applications. It is our intent that this work will motivate future research about how to design more flexible and robust object proposal algorithms for the robotics community.