Video-object segmentation and 3D-trajectory estimation for monocular video sequences
In this paper, we describe a video-object segmentation and 3D-trajectory estimation method for the analysis of dynamic scenes from a monocular uncalibrated view. Based on the color and motion information among video frames, our proposed method segments the scene, calibrates the camera, and calculates the 3D trajectories of moving objects. It can be employed for video-object segmentation, 2D-to-3D video conversion, video-object retrieval, etc. In our method, reliable 2D feature motions are established by comparing SIFT descriptors among successive frames, and image over-segmentation is achieved using a graph-based method. Then, the 2D motions and the segmentation result iteratively refine each other in a hierarchically structured framework to achieve video-object segmentation. Finally, the 3D trajectories of the segmented moving objects are estimated based on a local constant-velocity constraint, and are refined by a Hidden Markov Model (HMM)-based algorithm. Experiments show that the proposed framework can achieve a good performance in terms of both object segmentation and 3D-trajectory estimation.
video-object segmentation 2d-to-3d video conversion 3d trajectory estimation