This paper presents a high-performance FPGA solution to generic shape-based object detection in images. The underlying detection method involves representing the target object by binary templates containing positional and directional edge information. A particular scene image is preprocessed by edge segmentation, edge cleaning and distance transforms. Matching involves correlating the templates with the distance-transformed scene image and determining the locations where the mismatch is below a certain user-defined threshold. Although successful in the past, a significant drawback of these matching methods has been their large computational cost when implemented on a sequential general-purpose processor.

In this paper, we present a step by step implementation of the components of such object detection systems, taking advantage of the data and logical parallelism opportunities offered by an FPGA architecture. The realization of a pipelined calculation of the preprocessing and correlation on FPGA is presented in detail.