[3P5-OS-17a] OS-17

[3P5-OS-17a-02] Unsupervised Moving Object Segmentation and Ego-Velocity Prediction for Autonomous Vehicles

〇ISRAR ULHAQ¹, Phan Thi Huyen Thanh¹, Yuichiro Yoshimura¹, Truong Vinh Truong Duy¹, Naotake Natori¹ (1. AISIN CORPORATION)

Keywords:Autonomous driving, Motion segmentation, Speed estimation, Unsupervised learning

Motion segmentation in computer vision is a challenging task, particularly in the context of self-driving vehicles where backgrounds are constantly changing. Accurately detecting moving objects is crucial for effective vehicle control. To address this, we propose an innovative approach called Unsupervised Moving Object and Ego-Velocity Prediction (UMVP) specifically designed for autonomous vehicles. UMVP utilizes depth maps predicted from RGB images and trains a motion network using these depth maps and consecutive pairs of RGB frames. Additionally, it predicts the speed of the ego-vehicle by analyzing a pair of images. Our approach is completely unsupervised, eliminating the need for manual annotation or labeled data. We evaluated UMVP on the KITTI dataset, and observed significant improvements in motion segmentation, depth estimation compared to the baseline method. These results highlight the potential of UMVP to enhance motion segmentation in autonomous vehicles.