Dynamics Enhanced Multi-Camera Motion Segmentation From Unsynchronized Videos

Xikang Zhang, Bengisu Ozbay, Mario Sznaier, Octavia Camps; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 4668-4676

This paper considers the multi-camera motion segmentation problem using unsynchronized videos. Specifically, given two video clips containing several moving objects, captured by unregistered, unsynchronized cameras with different viewpoints, our goal is to assign features to moving objects in the scene. This problem challenges existing methods, due to the lack of registration information and correspondences across cameras. To solve it, we propose a new method that exploits both shape and dynamical information and does not require spatio-temporal registration or shared features. As shown in the paper, the combination of shape and dynamical information results in improved performance even in the single camera case, and allows for solving the multi-camera segmentation problem with a computational cost similar to that of existing single-view techniques. These results are illustrated using both the existing Hopkins 155 data set and a new multi-camera data set, the RSL-12.