FPGA Accelerated Online Boosting for Multi-target Tracking

Abstract

Robust real time tracking of multiple targets is a requisite feature for many applications. Online boosting has become an effective approach for dealing with the variability in object appearance. This approach can adapt its classifier to changes in appearance at the cost of additional runtime computation. In this paper, we address the task of accelerating online boosting for multiple target tracking. We propose a FPGA hardware accelerated architecture to evaluate and train a boosted classifier in real time. A general purpose CPU based software-only implementation can track a single target at 17 frames per second (FPS). The FPGA accelerated design is capable of tracking a single target at 1160 FPS or 57 independent targets at 30 FPS. This represents a 68× speed up over software.