Object detection represents one of the most important and challenging task in computer vision applications. Boosting-based approaches deal with computational intensive operations and they involve several sequential tasks that make very difficult developing hardware implementations with high parallelism level. This work presents a new hardware architecture able to perform object detection based on a cascade classifier in real-time and resource-constrained systems. As case study, the proposed architecture has been tailored to accomplish the face detection task and integrated within a complete heterogeneous embedded system based on a Xilinx Zynq-7000 FPGA-based System-on-Chip. Experimental results show that, thanks to the proposed parallel processing scheme and the runtime adaptable strategy to slide sub-windows across the input image, the novel design achieves a frame rate up to 125fps for the QVGA resolution, thus significantly outperforming previous works. Such a performance is obtained by using less than 10% of on-chip available logic resources with a power consumption of 377 mW at the 100 MHz clock frequency.

对象检测是计算机视觉应用程序中最重要和最具挑战性的任务之一。基于Boosting的方法处理计算密集型操作，它们涉及多个顺序任务，这些任务使开发具有高并行度的硬件实现非常困难。这项工作提出了一种新的硬件体系结构，该体系结构能够在实时且资源受限的系统中基于级联分类器执行对象检测。作为案例研究，所提出的体系结构经过了调整，可以完成人脸检测任务，并集成在基于基于Xilinx Zynq-7000 FPGA的片上系统的完整异构嵌入式系统中。实验结果表明，由于提出了并行处理方案和运行时自适应策略，可以在输入图像上滑动子窗口，新颖的设计为QVGA分辨率实现了高达125fps的帧速率，因此大大优于以前的作品。通过使用少于10％的片上可用逻辑资源（在100 MHz时钟频率下的功耗为377 mW），可以获得这种性能。