Visual Tracking Employing Maple Code Generation

Closed-loop, model-based visual target recognition and tracking in extreme lighting conditions is expensive to develop and computationally resource-intensive. To reduce the development cycle, improve software reliability and reduce the computational requirements for such algorithms, we have adapted Maple code generation to the problem of automatically generating efficient implementations of families of Newton solvers, each of which estimates a set of related parameters in a target model. We describe the leading target model in detail, formulate target identification as an optimization problem, explain the challenges in solving this model and the resulting need for multiple solvers, and the main advantage provided by code generation in Maple. We also discuss the problem of partially-saturated images in this scheme and our approach to solving it, and desirable features for a future version of Maple which would improve the applicability to similar application domains and simplify the implementation of code generators.