A Novel Low-cost FPGA-based Real-time Object Tracking System
This addresses the problem of real-time object tracking for applications requiring low-cost and efficient hardware, though it appears incremental as it builds on existing methods like Camshift.
The paper tackles the high computational cost and power consumption of CPU/GPU-based visual object tracking systems by proposing a novel algorithm combining a binary classifier and Kalman predictor, and a low-cost FPGA-based hardware architecture, achieving about 48% accuracy and 309 frames per second on the OTB benchmark.
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.