A Novel Low-cost FPGA-based Real-time Object Tracking System

TL;DR

This paper introduces a low-cost FPGA-based system for real-time object tracking that reduces computational costs and achieves high speed and robustness, outperforming traditional CPU or GPU systems.

Contribution

The paper presents a novel FPGA hardware architecture and an improved tracking algorithm combining binary classifiers and Kalman prediction for efficiency.

Findings

Achieves 48% accuracy on OTB benchmark

Operates at approximately 309 frames per second

Demonstrates high stability and robustness

Abstract

In current visual object tracking system, the CPU or GPU-based visual object tracking systems have high computational cost and consume a prohibitive amount of power. Therefore, in this paper, to reduce the computational burden of the Camshift algorithm, we propose a novel visual object tracking algorithm by exploiting the properties of the binary classifier and Kalman predictor. Moreover, we present a low-cost FPGA-based real-time object tracking hardware architecture. Extensive evaluations on OTB benchmark demonstrate that the proposed system has extremely compelling real-time, stability and robustness. The evaluation results show that the accuracy of our algorithm is about 48%, and the average speed is about 309 frames per second.