Improving FPGA accelerated tracking with multiple online trained classifiers

Abstract

Robust real time tracking is a requirement for many emerging applications. Many of these applications must track objects even as their appearance changes. Training classifiers online has become an effective approach for dealing with variability in object appearance. Classifiers can learn and adapt to changes online at the cost of additional runtime computation. In this paper, we propose a FPGA accelerated design of an online boosting algorithm that uses multiple classifiers to track and recover objects in real time. Our algorithm uses a novel method for training and comparing pose-specific classifiers along with adaptive tracking classifiers. Our FPGA accelerated design is able to track at 60 frames per second while concurrently evaluating 11 classifiers. This represents a 30× speed up over a CPU based software implementation. It also demonstrates tracking accuracy at state of the art levels on a standard set of videos.