Integrative embedded car detection system with DPM

Abstract

In this paper a embedded system based on CPU and FPGA integration is presented for car detection with the improved Deformable Part Model (Deformable Part Model, DPM). Original images are computed and layered into multi-resolution HOG feature pyramid on CPU, and then transmitted to FPGA for fast convolution operations, and finally return to CPU for statistical matching and display. Due to the architecture of the DPM algorithm, combined with the hardware characteristics of the embedded system, the overall algorithm frameworks are simplified and optimized. According to the mathematical derivation and statistical rules, the feature dimensions and the pyramid levels of the model descend without sacrificing the accuracy, which effectively reduce the amount of calculation and data transmission. The advantages of parallel processing and pipeline design of FPGA are made full used to achieve the acceleration of convolution computation, which significantly reduce the running time of the program. Several experiments have been done for visible images in the unmanned aerial vehicle’s view and the driver assistance scene, and infrared images captured in an overlooking perspective are also tested and analyzed. The result shows that the system has good real-time and accuracy performance in different situations.