Abstract
In this paper, the problem of non-rigid structure estimation in trajectory space from monocular vision is investigated. Similar to the Point Trajectory Approach (PTA), based on characteristic points’ trajectories described by a predefined Discrete Cosine Transform (DCT) basis, the structure matrix was also calculated by using a factorization method. To further optimize the non-rigid structure estimation from monocular vision, the rank minimization problem about structure matrix is proposed to implement the non-rigid structure estimation by introducing the basic low-rank condition. Moreover, the Accelerated Proximal Gradient (APG) algorithm is proposed to solve the rank minimization problem, and the initial structure matrix calculated by the PTA method is optimized. The APG algorithm can converge to efficient solutions quickly and lessen the reconstruction error obviously. The reconstruction results of real image sequences indicate that the proposed approach runs reliably, and effectively improves the accuracy of non-rigid structure estimation from monocular vision.
Highlights
Non-rigid structure estimation from monocular vision, which can recover the time varyingRehan et al [10] proposed a novel constraint in the form of local rigidity, which gave stable results in challenging realistic scenarios with small camera motions and shorter sequences
The experimental results on real image sequences indicate that the proposed approach effectively improves the accuracy of non-rigid structure estimation from monocular vision
The experimental dataset consists of a 307-frame sequence of a human practicing yoga, which comes from http://cvlab.lums.edu.pk/non-rigid structure estimation
Summary
Non-rigid structure estimation from monocular vision, which can recover the time varyingRehan et al [10] proposed a novel constraint in the form of local rigidity, which gave stable results in challenging realistic scenarios with small camera motions and shorter sequences. Introduced new constraints that were more effective for non-rigid structure estimation, which constrained the motion parameters so that the 3D shapes were most closely aligned to each other, making the rank constraints unnecessary. They proposed a new probabilistic model in [12], which incorporated the smoothness constraint without requiring any prior knowledge. This approach regarded the sequence of 3D shapes as a simple stationary Markov process with Procrustes alignment, whose parameters were learned during the fitting process. The underlying principle behind most approaches was to model deformations using a low-rank shape [2,9,14,15], and it improved the accuracy of the non-rigid structure estimation
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