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An Embedded Pedestrian Classifier for Automotive Applications

A hardware-friendly version of the well-known Support Vector Machine (SVM) is presented in this work, useful to implement its feed-forward phase on resource-limited architectures, such as Field Programmable Gate Arrays (FPGAs) or microcontrollers. In many embedded applications, a floating-point unit is seldom available, forcing a hardware-oriented reformulation. The system is tested on a pedestrian classification benchmarking dataset, showing how this proposal can be effectively applied in an automotive application. A modified version of the dataset is used in order to test the reliability to possible hardware artifacts.