One-Class Multiple Instance Learning and Applications to Target Tracking

Abstract

Existing work in the field of Multiple Instance Learning (MIL) have only looked at the standard two-class problem assuming both positive and negative bags are available. In this work, we propose the first analysis of the one-class version of MIL problem where one is only provided input data in the form of positive bags. We also propose an SVM-based formulation to solve this problem setting. To make the approach computationally tractable we further develop a iterative heuristic algorithm using instance priors. We demonstrate the validity of our approach with synthetic data and compare it with the two-class approach. While previous work in target tracking using MIL have made certain run-time assumptions (such as motion) to address the problem, we generalize the approach and demonstrate the applicability of our work to this problem domain. We develop a scene prior modeling technique to obtain foreground-background priors to aid our one-class MIL algorithm and demonstrate its performance on standard tracking sequences.