Camera On-boarding for Person Re-identification using Hypothesis Transfer Learning

Most of the existing approaches for person re-identification consider a static setting where the number of cameras in the network is fixed. An interesting direction, which has received little attention, is to explore the dynamic nature of a camera network, where one tries to adapt the existing re-identification models after on-boarding new cameras, with little additional effort. There have been a few recent methods proposed in person re-identification that attempt to address this problem by assuming the labeled data in the existing network is still available while adding new cameras. This is a strong assumption since there may exist some privacy issues for which one may not have access to those data. Rather, based on the fact that it is easy to store the learned re-identifications models, which mitigates any data privacy concern, we develop an efficient model adaptation approach using hypothesis transfer learning that aims to transfer the knowledge using only source models and limited labeled data, but without using any source camera data from the existing network. Our approach minimizes the effect of negative transfer by finding an optimal weighted combination of multiple source models for transferring the knowledge. Extensive experiments on four challenging benchmark datasets with a variable number of cameras well demonstrate the efficacy of our proposed approach over state-of-the-art methods.Comment: Accepted to CVPR 202

GLOBAL MULTIVIEW REGISTRATION USING NON-CONVEX ADMM

We consider the problem of aligning multiview scans obtained using a range scanner. The computational pipeline for this problem can be divided into two phases: (i) finding point-to-point correspondences between overlapping scans, and (ii) registration of the scans based on the correspondences. The focus of this work is on global registration in which the scans (modeled as point clouds) are required to be jointly registered in a common reference frame. We consider an optimization framework for global registration that is based on rank-constrained semidefinite programming. We propose to solve this semidefinite program using a non-convex variant of the ADMM (Alternating Direction Method of Multipliers) algorithm. This results in an efficient and scalable iterative method that requires just one eigendecompostion per iteration. We present simulations results on synthetic 3D models, using both clean and noisy correspondences. An interesting finding is that the algorithm is robust to wrong correspondences-it yields high-quality reconstructions even when a significant fraction of the correspondences are corrupted. Finally, by using ICP to infer the correspondences, we present some promising preliminary results for multiview reconstruction

Least-squares registration of point sets over SE(d) using closed-form projections

Consider the problem of registering multiple point sets in some d-dimensional space using rotations and translations. Assume that there are sets with common points, and moreover the pairwise correspondences are known for such sets. We consider a least-squares formulation of this problem, where the variables are the transforms associated with the point sets. The present novelty is that we reduce this nonconvex problem to an optimization over the positive semidefinite cone, where the objective is linear but the constraints are nevertheless nonconvex. We propose to solve this using variable splitting and the alternating directions method of multipliers (ADMM). Due to the linearity of the objective and the structure of constraints, the ADMM subproblems are given by projections with closed-form solutions. In particular, for m point sets, the dominant cost per iteration is the partial eigendecomposition of an and x and matrix, and m - 1 singular value decompositions of d x d matrices. We empirically show that for appropriate parameter settings, the proposed solver has a large convergence basin and is stable under perturbations. As applications, we use our method for 2D shape matching and 3D multiview registration. In either application, we model the shapes/scans as point sets and determine the pairwise correspondences using ICP. In particular, our algorithm compares favorably with existing methods for multiview reconstruction in terms of timing and accuracy

A Scalable ADMM Algorithm for Rigid Registration

A fundamental problem that comes up in computer vision, image processing, manifold learning, and sensor networks is that of registering multiple point sets using rigid transforms. A standard result in this regard is that the least-square formulation of the registration problem admits a closed-form solution for two point sets. However, since the group of rigid transforms is not convex, solving the least-square optimization for multiple point sets is computationally challenging. It was recently demonstrated that the least-square formulation can be relaxed into a tractable semidefinite program, and that the relaxation is provably tight under certain assumptions. The difficulty is that standard solvers for semidefinite programming (e.g., interior-point solvers) cannot be scaled to handle large-sized problems. In this letter, we propose an iterative solver based on variable splitting and the alternating direction method of multipliers. Since each iteration essentially involves an eigendecomposition, the proposed solver can be scaled to problems that are beyond the reach of interior-point solvers. We present results on simulated and real data to demonstrate the potential of the solver