Pedestrian recognition method based on Jetson nano

This paper presents a novel pedestrian recognition method based on Jetson Nano, leveraging its advantages as an edge computing device with low latency and high privacy. Traditional methods for pedestrian recognition often require substantial computing resources and a strong network connection, making them unsuitable for edge devices. Thus, this paper proposes an edge computing method specifically designed for Jetson Nano, utilizing CUDA and TensorRT to achieve higher performance. This method utilizes a convolutional neural network to extract image features, which are then activated through Reshape, Transpose, and Sigmoid functions. The Slice operation is employed to extract objects within areas with relatively high probabilities. The position of the object is then adjusted using mathematical operations such as Mul, Sub, Add, Pow, etc., resulting in more accurate detection results. This paper demonstrates the effectiveness of using Jetson Nano as an edge computing device for pedestrian recognition and the potential of CUDA and TensorRT for optimizing performance on edge devices. The proposed method has broad applications in various fields, such as surveillance systems, autonomous vehicles, and robotics, where real-time pedestrian recognition tasks are essential.