Abstract

Motion is one of the most important features describing an image sequence. Motion estimation has been widely applied in structure from motion, vision-based navigation and many other fields. However, real-time motion estimation remains a challenge because of its high computational expense. The traditional CPU-based scheme cannot satisfy the power, size and computation requirements in many applications. With the availability of new parallel architectures such as FPGAs and GPUs, applying these new technologies to computer vision tasks such as motion estimation has been an active research field in recent years. In this dissertation, FPGAs have been applied to real-time motion estimation for their outstanding properties in computation power, size, power consumption and reconfigurability. It is believed in this dissertation that simply migrating the software-based algorithms and mapping them to a specific architecture is not enough to achieve good performance. Accuracy is usually compromised as the cost of migration. Improvement and optimization at the algorithm level are critical to performance. To improve motion estimation on the FPGA platform and prove the effectiveness of the method, three main efforts have been made in the dissertation. First, a lightweight tensor-based algorithm has been designed which can be implemented in a fully pipelined structure. Key factors determining the algorithm performance are analyzed from the simulation results. Second, an improved algorithm is then developed based on the analyses of the first algorithm. This algorithm applies a ridge estimator and temporal smoothing in order to improve the accuracy. A structure composed of two pipelines is designed to accommodate the new algorithm while using reasonable hardware resources. Third, a hardware friendly algorithm is developed to analyze the optical flow field and detect obstacles for unmanned ground vehicle applications. The motion component is de-rotated, de-translated and postprocessed to detect obstacles. All these steps can be efficiently implemented in FPGAs. The characteristics of the FPGA architecture are taken into account in all development processes of these three algorithms. This dissertation also discusses some important perspectives for FPGA-based design in different chapters. These perspectives include software simulation and optimization at the algorithm development stage, hardware simulation and test bench design at the hardware development stage. They are important and particular for the development of FPGA-based computer vision algorithms. The experimental results have shown that the proposed motion estimation module can perform in real-time and achieve over 50% improvement in the motion estimation accuracy compared to the previous work in the literature. The results also show that the motion field can be reliably applied to obstacle detection tasks.

Degree

PhD

College and Department

Ira A. Fulton College of Engineering and Technology; Electrical and Computer Engineering

Rights

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