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.
@article{arxiv.1804.05535,
title = {A Novel Low-cost FPGA-based Real-time Object Tracking System},
author = {Peng Gao and Ruyue Yuan and Zhicong Lin and Linsheng Zhang and Yan Zhang},
journal= {arXiv preprint arXiv:1804.05535},
year = {2018}
}