Pedestrian detection systems are increasing in popularity recently. These systems that work together with car-mounted cameras need to operate in real-time. A Field Programmable Gate Array (FPGA) is able to work in a highly optimized parallel process and hence it is expected to work in real-time. However, it is difficult for FPGA to calculate complex processes. Therefore, pedestrian detection methods must have low computational costs in order to implement the system using FPGA. This paper proposes a system that uses Binary Robust Independent Elementary Features (BRIEF) and a Neural Network (NN) as a pedestrian detection method. The system was simulated using a CPU and the human detection performance was evaluated. Additionally, the NN was trained using three Particle Swarm Optimization (PSO) methods. The performance of our approach was shown using a Receiver Operating Characteristic (ROC) curve with respect to each learning method. In the future, the system needs to improve the human detection rate and it will be implemented and simulated using an FPGA.

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