FPGA-based circular hough transform with graph clustering for vision-based multi-robot tracking

Shape-based object detection and recognition are frequently used methods in the field of computer vision. A well-known algorithm for circle detection is the Circular Hough Transform (CHT). This Hough Transform algorithm needs a huge memory space and large computational resources. Field Programmable Gate Array (FPGA)-based hardware accelerators can be used to efficiently handle such compute-intensive applications. In this paper, we present a resource-efficient FPGA-based architecture for the CHT algorithm. Additionally, we introduce a unique approach by combining the CHT algorithm with graph clustering. The combination of these algorithms and their implementation on a Xilinx Virtex-4 FPGA is used to support real-time vision-based multi-robot tracking. Furthermore, an efficient architecture is proposed to significantly reduce the required memory in the CHT module. For the Graph Clustering module, a multiplier-less distance calculation unit is implemented, significantly reducing the required FPGA resources. The proposed CHT design can handle multi-robot localization with an accuracy of 97 %, supporting a maximum video resolution of 1024x1024 with 128 frames per second, resulting in 134 MPixel/s. Our design provides significantly higher throughput compared to other implementations on embedded processors, FPGAs, and general purpose CPUs. Compared to an OpenCV implementation on a 3.2 GHz desktop CPU, our implementation achieves a speed- up of more than 5.7.