This paper presents a hardware architecture for face detection based system on AdaBoost algorithm using Haar features. We describe the hardware design techniques including image scaling, integral image generation, pipelined processing as well as classifier, and parallel processing multiple classifiers to accelerate the processing speed of the face detection system. Also we discuss the optimization of the proposed architecture which can be scalable for configurable devices with variable resources. The proposed architecture for face detection has been designed using Verilog HDL and implemented in Xilinx Virtex-5 FPGA. Its performance has been measured and compared with an equivalent software implementation. We show about 35 times increase of system performance over the equivalent software implementation.
Abstract-This paper presents a parallelized architecture of multiple classifiers for face detection based on the Viola and Jones object detection method. This method makes use of the AdaBoost algorithm which identifies a sequence of Haar classifiers that indicate the presence of a face. We describe the hardware design techniques including image scaling, integral image generation, pipelined processing of classifiers, and parallel processing of multiple classifiers to accelerate the processing speed of the face detection system. Also we discuss the parallelized architecture which can be scalable for configurable device with variable resources. We implement the proposed architecture in Verilog HDL on a Xilinx Virtex-5 FPGA and show the parallelized architecture of multiple classifiers can have 3.3× performance gain over the architecture of a single classifier and an 84× performance gain over an equivalent software solution.
Abstract-We present a method for implementing high speed Finite Impulse Response (FIR) filters using just registered adders and hardwired shifts. We extensively use a modified common subexpression elimination algorithm to reduce the number of adders. We target our optimizations to Xilinx Virtex II devices where we compare our implementations with those produced by Xilinx CoregenTM using Distributed Arithmetic. We observe up to 50% reduction in the number of slices and up to 75% reduction in the number of LUTs for fully parallel implementations. We also observed up to 50% reduction in the total dynamic power consumption of the filters. Our designs perform significantly faster than the MAC filters, which use embedded multipliers.
We present a method for implementing high speed finite impulse response (FIR) filters on field programmable gate arrays (FPGAs). Our algorithm is a multiplierless technique where fixed coefficient multipliers are replaced with a series of add and shift operations. The first phase of our algorithm uses registered adders and hardwired shifts. Here, a modified common subexpression elimination (CSE) algorithm reduces the number of adders while maintaining performance. The second phase optimizes routing delay using prelayout wire length estimation techniques to improve the final placed and routed design. The optimization target platforms are Xilinx Virtex FPGA devices where we compare the implementation results with those produced by Xilinx Coregen, which is based on distributed arithmetic (DA). We observed up to 50% reduction in the number of slices and up to 75% reduction in the number of look up tables (LUTs) for fully parallel implementations compared to DA method. Also, there is 50% reduction in the total dynamic power consumption of the filters. Our designs perform up to 27% faster than the multiply accumulate (MAC) filters implemented by Xilinx Coregen tool using DSP blocks. For placement, there is a saving up to 20% in number of routing channels. This results in lower congestion and up to 8% reduction in average wirelength.
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