Fast FPGA-Based Multiobject Feature Extraction

This paper describes a high-frame-rate (HFR) vision system that can extract locations and features of multiple objects in an image at 2000 f/s for 512 × 512 images by implementing a cell-based multiobject feature extraction algorithm as hardware logic on a field-programmable gate array-based high-speed vision platform. In the hardware implementation of the algorithm, 25 higher-order local autocorrelation features of 1024 objects in an image can be simultaneously extracted for multiobject recognition by dividing the image into 8 × 8 cells concurrently with calculation of the zeroth and first-order moments to obtain the sizes and locations of multiple objects. Our developed HFR multiobject extraction system was verified by performing several experiments: tracking for multiple objects rotating at 16 r/s, recognition for multiple patterns projected at 1000 f/s, and recognition for human gestures with quick finger motion.