  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.
