Long-Range Motion Trajectories Extraction of Articulated Human Using Mesh Evolution
This work addresses the challenge of reliable motion tracking for articulated humans in video sequences, which is incremental as it builds on existing methods by incorporating spatial constraints.
The paper tackles the problem of extracting dense, long-range motion trajectories for articulated humans in videos by considering spatial structure through mesh evolution, resulting in favorable performance in accuracy and integrity compared to state-of-the-art methods.
This letter presents a novel approach to extract reliable dense and long-range motion trajectories of articulated human in a video sequence. Compared with existing approaches that emphasize temporal consistency of each tracked point, we also consider the spatial structure of tracked points on the articulated human. We treat points as a set of vertices, and build a triangle mesh to join them in image space. The problem of extracting long-range motion trajectories is changed to the issue of consistency of mesh evolution over time. First, self-occlusion is detected by a novel mesh-based method and an adaptive motion estimation method is proposed to initialize mesh between successive frames. Furthermore, we propose an iterative algorithm to efficiently adjust vertices of mesh for a physically plausible deformation, which can meet the local rigidity of mesh and silhouette constraints. Finally, we compare the proposed method with the state-of-the-art methods on a set of challenging sequences. Evaluations demonstrate that our method achieves favorable performance in terms of both accuracy and integrity of extracted trajectories.