CNN-based multi-frame IMO detection from a monocular camera
Nolang Fanani, Matthias Ochs, Alina Stürck, Rudolf Mester · 2018
This paper presents a method for detecting independently moving objects (IMOs) from a monocular camera mounted on a moving car. A CNN-based classifier is employed to generate IMO candidate patches; independent motion is detected by geometric criteria on keypoint trajectories in these patches. Instead of looking only at two consecutive frames, we analyze keypoints inside the IMO candidate patches through multi-frame epipolar consistency checks. The obtained motion labels (IMO/static) are then propagated over time using the combination of motion cues and appearance-based information of the IMO candidate patches. We evaluate the performance of our method on the KITTI dataset, focusing on sub-sequences containing IMOs.