Pedestrian detection for advanced driving assisting system: a transfer learning approach

Abstract

pedestrian detection is an important task that must be integrated into an advanced driving assisting system (ADAS). For a pedestrian detection task many rules must be respected like high performance, real-time processing, and lightweight size to fit into the embedded device of the ADAS. In this paper, we propose a pedestrian detection system based on a convolutional neural network (CNN). CNN is a deep learning model generally used for computer vision tasks like classification and detection because of its power in image processing and decision making. The proposed CNN model is named Yolov3 tiny. It was firstly used for general object detection. In this work, we applied the transfer learning technique on the proposed CNN model to make it suitable for pedestrian detection. The pedestrian detection dataset Caltech US was used to train and evaluate the proposed model. The model achieves an average precision of 76.7% and an inference time of 202 FPS.