A Novel Hardware Algorithm for Real-time Image Recognition Based on Real AdaBoost Classification

Real-time object detection is important for surveillance applications. This paper describes a high-performance object detector using a commercially available FPGA. Major bottlenecks in the real AdaBoost classifier are resolved. A new FIR-filter-like hardware architecture takes advantage of an FPGA's hardware parallelism and block-RAM structure. The resulting design uses Xilinx Virtex 5 and achieves the real-time processing performance of 220 f/s at 201 MHz and adjustable recognition performance with a variable number of weak classifiers. This is the first demonstration of a histogram of oriented gradients and Real AdaBoost detector on an FPGA.