Discrete Optimization Algorithms in Real-Time Visual Tracking

Abstract

In this work we introduce a novel formulation of the association problem in visual tracking systems as a discrete optimization problem. The full data association problem is formulated as a search for the best tracking configuration to match hypothesis. We have implemented three local search algorithms: Hill Climbing, Simulated Annealing, and Tabu Search algorithms. These algorithms are guided by heuristic evaluation function which takes into account structural and specific information such as distance and shape. We have also introduced a novel technique in order to achieve incrementality in discrete optimization algorithms searching on an indirect space. We will show how this incrementality produces efficient and fast results, especially so when real-time is a hard constraint. The results obtained with the three discrete optimization algorithms are compared with other well-known and powerful computer vision tracking algorithms. We will prove the effectiveness and robustness of the discrete optimization algorithms in five different real-life scenarios.