Efficient migration of complex off-line computer vision software to real-time system implementation on generic computer hardware.

This paper addresses the problem of migrating large and complex computer vision code bases that have been developed off-line, into efficient real-time implementations avoiding the need for rewriting the software, and the associated costs. Creative linking strategies based on Linux loadable kernel modules are presented to create a simultaneous realization of real-time and off-line frame rate computer vision systems from a single code base. In this approach, systemic predictability is achieved by inserting time-critical components of a user-level executable directly into the kernel as a virtual device driver. This effectively emulates a single process space model that is nonpreemptable, nonpageable, and that has direct access to a powerful set of system-level services. This overall approach is shown to provide the basis for building a predictable frame-rate vision system using commercial off-the-shelf hardware and a standard uniprocessor Linux operating system. Experiments on a frame-rate vision system designed for computer-assisted laser retinal surgery show that this method reduces the variance of observed per-frame central processing unit cycle counts by two orders of magnitude. The conclusion is that when predictable application algorithms are used, it is possible to efficiently migrate to a predictable frame-rate computer vision system.